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In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. Later, container workloads based on Docker and Podman were orchestrated via Kubernetes or OpenShift. On the other hand, high-performance computing (HPC) environments have been lagging in this process, as much work is still needed to figure out how to apply containerization platforms for HPC. Containers have many advantages, as they tend to have less overhead while providing flexibility, modularity, and maintenance benefits. This makes them well-suited for tasks requiring a lot of computing power that are latency- or bandwidth-sensitive. But they are complex to manage, and many daily operations are based on command-line procedures that take years to master. This paper proposes a different architecture based on seamless hardware integration and a user-friendly UI (User Interface). It also offers dynamic workload placement based on real-time performance analysis and prediction and Machine Learning-based scheduling. This solves a prevalent issue in Kubernetes: the suboptimal placement of workloads without needing individual workload schedulers, as they are challenging to write and require much time to debug and test properly. It also enables us to focus on one of the key HPC issues—energy efficiency. Furthermore, the application we developed that implements this architecture helps with the Kubernetes installation process, which is fully automated, no matter which hardware platform we use—x86, ARM, and soon, RISC-V. The results we achieved using this architecture and application are very promising in two areas—the speed of workload scheduling and workload placement on a correct node. This also enables us to focus on one of the key HPC issues—energy efficiency. [ABSTRACT FROM AUTHOR]

The Industrial Metaverse paradigm can be broadly described as a virtual environment that integrates various technologies such as augmented reality and mixed reality to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the Industrial Metaverse to drive sustainability and resource efficiency is compelling. Using advanced technologies such as the Industrial Metaverse is vital in an endeavor to have a competitive edge in a rapidly evolving business environment. However, the environmental impact of the technologies underpinning the Industrial Metaverse, like data centers and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that, by 2025, without sustainable artificial intelligence (AI) practices, AI will consume more energy than the human workforce, significantly offsetting zero carbon gains. As the Metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. Moreover, the specific extent to which this substitution can alleviate environmental concerns remains an open issue, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the Industrial Metaverse, as well as explore the environmental impact of the Industrial Metaverse. The integrative literature review design and methodological approach involved multiple sources from the Web of Science and databases such as the ACM library, IEEE Library, and Google Scholar, which were analyzed to provide a comprehensive understanding of the developments in the Industrial Metaverse. Firstly, by considering the Industrial Metaverse's architecture, we elucidate the Industrial Metaverse concept and the associated enabling technologies. Secondly, we performed an exploration through a discussion of the prevalent use cases and the deployment of the emerging Industrial Metaverse. Thirdly, we explored the impact of the Industrial Metaverse on the environment. Lastly, we address novel security and privacy risks, as well as upcoming research challenges, keeping in mind that the Industrial Metaverse is based on a strong data fabric. The results point to the Industrial Metaverse as having both positive and negative environmental effects in terms of energy consumption, e-waste, and pollution. Research, however, indicates that most Industrial Metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Finally, for sustainability in the Industrial Metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, we examined the effects of products on the environment, as well as in the creation of a circular economy. [ABSTRACT FROM AUTHOR]

Sensor networks generate large volumes of data that require robust processing, storage, and analysis capabilities provided by data centers. Software-defined networking, as a new network technique for designing and building networks, provides new concepts and solutions for the current data center system construction. However, the software-defined networking (SDN) framework based on the OpenFlow protocol cannot support new protocols or actively add unknown protocols, since they cause the control plane protocol to become more bloated. It is difficult to solve the problem of network scalability because components are being continuously redesigned. Therefore, an optimized source routing technology is proposed in combination with the protocol-oblivious forwarding technology. Furthermore, a low-cost and extensible network switch is designed to realize the independent data forwarding mechanism that effectively reduces the amount of network control signaling. The experimental results show that the scheme can significantly reduce the scale of flow table items and improve the hardware forwarding rates as well as the flexibility of forwarding rules. Moreover, the design scheme has a good application prospect. [ABSTRACT FROM AUTHOR]

Agriculture is dwindling all across the world, which has an impact on ecosystem production capacity. Problems with resource allocation are being investigated as a result of this study. To put it another way, the test's main objective is to create an optimal allocation arrangement by proposing a novel method for overcoming resource allocation issues and challenges. This was made possible by using an intelligent optimisation technique that is both robust and distinctive. There is an urgent need to resolve the issue in the domain so that it may reclaim its vitality and resume its upward trajectory. A revolutionary two-tier approach is designed in this study endeavour to aid farmers in continual field monitoring. The proposed model is divided into two parts: (a) the land-subsystem and (b) the cloud user-subsystem. The agricultural area has been outfitted with several IoT sensor nodes for monitoring "soil PH level, water level, temperature, humidity, moisture, weeding, nutrition, variable spraying, salinity, and rainfall". These sensors are grouped together, and the Cluster Head (CH) connects the clustered nodes to the Base Station (BS). The CH is chosen using the newly proposed Multi-Objective Modified Emperor Penguin Optimization (MEPO) approach, which takes into account many factors such as remaining energy, distance, latency, and QoS. The land-based data is continually stored in the cloud server through the gateway. The cloud sub-system, on the other hand, encompasses the farmers, Physical Machine (PM), and Virtual Machine (VM). The PM assigns a certain VM to process the required work based on the request received from the farmers. Furthermore, the Lionized Golden Eagle optimization (LGEO) based sixfold goal model is projected for optimum task allocation onto the VM, with a focus on power consumption, migration cost, memory utilization, response time and server load and execution time. The proposed model, as a whole, becomes suited for end-to-end farm monitoring. [ABSTRACT FROM AUTHOR]

Temperature and wind observations from aircraft are regarded of major importance for aviation meteorology and numerical weather prediction (NWP). The European Meteorological Aircraft Derived Data Center (EMADDC) system processes aircraft surveillance data received from air traffic control (ATC) and converts it into observations of wind and temperature. Only so-called Mode-S Enhanced Surveillance data can be used, because this data contains the air vector and ground vector of the aircraft, from which a wind vector can be inferred. To acquire high quality observations, the data is processed in three steps: pre-processing, processing, and post-processing. The pre-processing is needed to obtain high-quality information and to calculate several correction values such as temperature corrections and heading corrections. Processing converts the aircraft data into meteorological information and finally post-processing guarantees that only high-quality information is made available. The quality of the observations produced is verified by comparing these observation to other upper air wind and temperature observations from radiosondes and comparing them with NWP data. This paper presents the EMADDC system, operational since 2019. [ABSTRACT FROM AUTHOR]

Airborne measurements are pivotal for providing detailed, spatiotemporally resolved information about atmospheric parameters, and aerosol and cloud properties, thereby enhancing our understanding of dynamic atmospheric processes. For 30 years, the U.S. Department of Energy (DOE) Office of Science supported an instrumented Gulfstream-1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) user facility Data Center and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated dataset was recently developed covering the final six years of G-1 operations (2013 to 2018). The integrated dataset includes data collected from 236 flights (766.4 hours), which covered the Arctic, the U.S. Southern Great Plains (SGP), the U.S. West Coast, the Eastern North Atlantic (ENA), the Amazon Basin in Brazil, and the Sierras de Córdoba range in Argentina. These comprehensive data streams provide much-needed insight into spatiotemporal variability of thermodynamic quantities, aerosol and cloud states and properties for addressing essential science questions in Earth system process studies. This manuscript describes the DOE ARM merged G-1 datasets, including information on the acquisition, collection, and quality control processes. It further illustrates the usage of this merged dataset to evaluate the Energy Exascale Earth System Model (E3SM) with the Earth System Model Aerosol-Cloud Diagnostics (ESMAC Diags) package. [ABSTRACT FROM AUTHOR]

In this paper, we present a practical automated visual monitoring system designed to enhance the efficiency of visual inspection in data centers. Visual inspection is a manual process of detecting failures in electronic devices based on light-emitting diode (LED) lighting. The objective of data center monitoring is to implement real-time failure detection to prevent any service disruptions or loss of user data. To improve the reliability of data centers, we propose a monitoring system that automatically detects anomalies in electronic devices. The system integrates a digital camera and a novel algorithm that is tailored to distinguish normal LED lighting patterns from abnormal patterns. Experimental data were collected in an actual data center room and the system was evaluated with experiments involving LED region segmentation and anomaly detection. For the LED segmentation task, we propose a K-means-based method that outperformed a previous method based on background subtraction by 8%. For anomaly detection, recorded videos covering continuous monitoring of approximately 17 h were used. The proposed method successfully detected all five true anomalies in the video data. The results of another experiment for anomaly detection demonstrate that prolonged video recording for collecting patterns of LED lighting can positively contribute to a better understanding of normal patterns and can effectively be used to ensure the detection of device anomalies. [ABSTRACT FROM AUTHOR]

Dissolved Organic Carbon (DOC) is the largest organic carbon pool in the ocean. Considering the biotic and abiotic factors controlling DOC processes, indirect satellite methods for open ocean DOC estimation can be developed, using conceptual, empirical or statistical models, driven by multiple satellite products. In this study, we infer a time series of global DOC from data of the European Space Agency's (ESA) Ocean Colour Climate Change Initiative (OC-CCI) in combination with a global database of in situ DOC observations. We tested empirical machine learning modelling approaches in which the available in situ data are used to train the models and to find empirical relationships between DOC and variables available from remote sensing. Of the tested methods, a random forest regression showed the best results, and the details of this model are further reported here. We present a time series of global open ocean DOC concentrations between 2010-2018 that is made freely available through the archive of the UK Centre for Environmental Data Analysis (CEDA). [ABSTRACT FROM AUTHOR]

Summary: The rapid expansion of IoT systems has caused network congestion and delays in task placement and resource provisioning as usually the tasks are executed at a far location in the cloud. Fog computing reduces the computing burden of cloud data centers as well as the communication burden of the internet as fog resources are placed near the data generation points. Within Fog computing, an important challenge is the optimal task placement which is an NP‐class problem. This work applies machine learning for task clustering and addresses the task placement problem in a fog computing environment using a hybrid of two recent metaheuristics; Jaya and gray wolf optimization (GWO). The hybrid method considers optimizing the total number of active fog nodes, load balancing in fog nodes, and average response time of the tasks. The performance of the proposed method is evaluated on a real‐time LCG dataset and is compared with reinforcement learning fog scheduling (RLFS), genetic algorithm (GA), dynamic resource allocation mechanism (DRAM), load balancing and scheduling algorithm (LBSSA), and particle swarm optimization with simulated annealing (PSO‐SA) algorithms. The results demonstrate the superiority of the suggested method over the baseline techniques in terms of average improvement of 51.04% in load balance variance, 30.25% in average response time, 24.16% in execution time, and 47.10% in the number of devices used. [ABSTRACT FROM AUTHOR]

Data centers are a major source of carbon emissions and are subsequently contributing to global carbon footprints. Keeping in view of providing a sustainable solution to society, we have analyzed various factors that can help to achieve carbon neutrality and maximum sustainability. Our study pointed towards the need to follow a sustainable approach for incoming workflow throughout the life-cycle of Data centers. We analyzed that workloads need to be segregated before assigning them to the data centers so that energy-efficient resource allocation could be done. This paper demonstrates unsupervised learning techniques to cluster the incoming cloud workloads. The heterogeneous workloads were characterized using machine learning approaches and appropriate clusters were crafted. For analysis, Google Cluster Dataset is used. In order to improve the accuracy, data were normalized, and random samples of data were selected for clustering. The machine learning algorithms applied were able to successfully determine the appropriate clusters that can further be used for energy-efficient resource scheduling. [ABSTRACT FROM AUTHOR]

This article explores the potential use of molybdenum as a catalyst in organic synthesis, specifically in cross-coupling reactions. The researchers discovered that a molybdenum species, created by reducing a molybdenum precursor with phenylmagnesium bromide, can activate carbon-halogen bonds and produce a cyclized dimerization product. The study highlights the potential of molybdenum catalysts in sustainable catalysis and their applications in cross-coupling reactions of aryl halides. The article also mentions the potential bioactivity of the resulting phthalan derivatives. [Extracted from the article]

This article discusses the synthesis of α-aryl carbonyl compounds using palladium catalysis. The authors focus on the deprotonative α-arylation of Meyers's chiral bicyclic lactams (MCBLs) as a potential method for synthesizing these compounds. They explore various bases and catalytic systems to optimize the reaction conditions and achieve high yields. The authors also investigate the reaction scope by testing different substrates and bridgehead substituents. The results suggest that steric effects play a crucial role in determining the reactivity of the substrates. The authors propose a plausible mechanism for the formation of the arylated lactam products. Overall, this study provides valuable insights into the synthesis of α-aryl carbonyl compounds and highlights the potential of MCBLs as versatile building blocks. [Extracted from the article]

This document provides information on the synthesis and characterization of various thiochromeno[2,3-c]pyrrole derivatives. The compounds were synthesized using specific reactions and their structures were confirmed using spectroscopic techniques. Crystallographic analysis was also performed on one of the compounds. The document includes detailed information on the synthesis, characterization, and analysis of each compound. [Extracted from the article]

High-quality ocean in situ profile observations are fundamental for ocean and climate research and operational oceanographic applications. Here we describe a new global ocean subsurface temperature profile database named the Chinese Academy of Science (CAS) Oceanography Data Center version 1 (CODC-v1). This database contains over 17 million temperature profiles between 1940–2023 from all available instruments. The major data source is the World Ocean Database (WOD), but CODC-v1 also includes some data from some Chinese institutes which are not available in WOD. The data are quality-controlled (QC-ed) by a new QC system that considers the skewness of local temperature distributions, topographic barriers, and the shift of temperature distributions due to climate change. Biases in Mechanical Bathythermographs (MBTs), eXpendable Bathythermographs (XBTs), and Bottle data (OSD) are all corrected using recently proposed correction schemes, which makes CODC-v1 a bias-corrected dataset. These aspects ensure the data quality of the CODC-v1 database, making it suitable for a wide spectrum of ocean and climate research and applications. [ABSTRACT FROM AUTHOR]

Telecommunication (telco) cloud services have emerged as crucial components in the modern digital landscape, offering extensive capabilities for data management, connectivity, and service provision. However, research on telco clouds lacks comprehensive data on the characteristics of production workloads, which is fundamental for designing effective resource management systems, such as workload schedulers and power management mechanisms. To this end, this paper addresses a substantial gap in telco cloud research by creating a comprehensive dataset that encapsulates crucial information regarding the pattern demands of applications within telco data centers. In addition, the proposed dataset contributes to the field by enabling strategic network configuration, optimizing data center sizing, facilitating proactive decision-making for data center operations, but its applicability extends beyond these cases. These examples illustrate the practical value of the dataset in enhancing efficiency, reducing operational costs, and ensuring optimal performance within telecommunication data centers. [ABSTRACT FROM AUTHOR]

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers. [ABSTRACT FROM AUTHOR]

The feedback of vegetation on cloud cover is an important link in the global water cycle. However, the relative importance of vegetation and related factors (surface properties, heat fluxes, and environmental conditions) on cloud cover in the context of greening remains unclear. Combining the Global Land Surface Satellite (GLASS) leaf area index (LAI) product and the fifth-generation reanalysis data of the European Centre for Medium-Range Weather Forecasts (ERA5), we quantified the relative contribution of vegetation and related factors to total cloud cover (TCC) in typical regions (Eastern European Plain, Western Siberian Plain, Mongolian Plateau, and Northeastern China Plain) of Eurasia over 21 years, and investigated how vegetation moderated the contribution of the other factors. Here, we show that the relative contribution of different factors to TCC was closely related to the climate and vegetation characteristics. In energy-limited (moisture-limited) areas, temperature (relative humidity) was more likely to be the factor that strongly contributed to TCC variation. Except for sparsely vegetated ecosystems, the relative contribution of LAI to TCC was stable within a range of 8–13%. The case study also shows that vegetation significantly modulated the contribution of other factors on TCC, but the degree of the regulation varied among different ecosystems. Our results highlight the important influence of vegetation on cloud cover during greening, especially the moderating role of vegetation on the contribution of other factors. [ABSTRACT FROM AUTHOR]

This article discusses the synthesis and reactivity of a new sulfonium salt that can transfer a sulfonitrene unit to C=C double bonds. The salt was synthesized on a large scale and is stable in air and water. It was used in two different transformations, expanding indenes to isoquinolines and contracting arylcyclobutenes to cyanocyclopropanes. The reagent is versatile and can tolerate various functional groups, making it a useful tool in skeletal editing. The authors acknowledge funding and provide supporting information for further details. [Extracted from the article]

The ever-growing demand for speed and bandwidth in data-centric applications necessitates innovative solutions to overcome existing bottlenecks. Traditional data centers reliant on electronic infrastructure often face limitations in speed. While the concept of optical data centers has emerged as a promising alternative, the practical implementation has been hindered by component unavailability, such as tunable wavelength converters and optical memory. Furthermore, the transition to fiber optic technology has not fully addressed issues like heat dissipation associated with copper cables. To address these challenges, the utilization of visible light communication (VLC) within an optical wireless medium has been proposed, aiming to reduce cabling complexities and mitigate heat generation. This paper presents a comprehensive design and analysis of a VLC-enabled optical wireless data center, leveraging Filter Bank Multicarrier (FBMC) modulation. Key considerations including peak-to-average power ratio, bit error rate and error vector magnitude are thoroughly examined, accompanied by the derivation of relevant mathematical models and presentation of simulation results. Through the integration of VLC-FBMC technology, this research contributes to the advancement of efficient and high-speed data center architectures, promising enhanced performance and scalability for future data-intensive applications. [ABSTRACT FROM AUTHOR]

Software-defined networks (SDNs) are expanding their presence beyond laboratories, campus networks, ISPs, and data centre networks, moving into various domains. Although originally designed for campus networks, SDNs face scalability challenges, especially with the use of OpenFlow. Addressing these challenges requires innovative traffic management mechanisms to efficiently handle the growing number of connected devices and the increasing volume of traffic from various types of applications. This article proposes an innovative method for link weight selection that incorporates multipath transmission and flow aggregation in the SDNs. This novel approach improves resource utilization in two key ways. First, it involves the preservation of bandwidth during congestion. Second, it minimizes internal resource usage, as illustrated by a reduction in the number of table entries in switches. Resources undergo optimization through the introduction of a novel mechanism for flow aggregation. This novel mechanism, coupled with multipath transmission, enables adaptive responses to dynamic changes in network conditions. The aggregation process leads to a reduced number of flow entries in the core switches compared to the conventional operation of OpenFlow. The proposed scenarios for link weight allocation allow for a reduction in the number of entries in the core switches by up to 99%. The application of the proposed method also results in an increase of 58% in traffic transmission. [ABSTRACT FROM AUTHOR]

The booming growth of the internet of things has brought about widespread deployment of devices and massive amounts of sensing data to be processed. Federated learning (FL)-empowered mobile edge computing, known for pushing artificial intelligence to the network edge while preserving data privacy in learning cooperation, is a promising way to unleash the potential information of the data. However, FL's multi-server collaborative operating architecture inevitably results in communication energy consumption between edge servers, which poses great challenges to servers with constrained energy budgets, especially wireless communication servers that rely on battery power. The device-to-device (D2D) communication mode developed for FL turns high-cost and long-distance server interactions into energy-efficient proximity delivery and multi-level aggregations, effectively alleviating the server energy constraints. A few studies have been devoted to D2D-enabled FL management, but most of them have neglected to investigate server computing power for FL operation, and they have all ignored the impact of dataset characteristics on model training, thus failing to fully exploit the data processing capabilities of energy-constrained edge servers. To fill this gap, in this paper we propose a D2D-assisted FL mechanism for energy-constrained edge computing, which jointly incorporates computing power allocation and dataset correlation into FL scheduling. In view of the variable impacts of computational power on model accuracy at different training stages, we design a partite graph-based FL scheme with adaptive D2D pairing and aperiodic variable local iterations of heterogeneous edge servers. Moreover, we leverage graph learning to exploit the performance gain of the dataset correlation between the edge servers in the model aggregation process, and we propose a graph-and-deep reinforcement learning-based D2D server pairing algorithm, which effectively reduces FL model error. The numerical results demonstrate that our designed FL schemes have great advantages in improving FL training accuracy under edge servers' energy constraints. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, artificial intelligence, etc., data centers have become a flexible load due to their adjustable capabilities and are able to take on power service. However, the existing communication architecture is monolithic and poorly adapted between services and communications, which poses a challenge for data centers to participate in power service. Firstly, this article constructs a multi-domain communication architecture for data center management systems that includes local and remote communication, and analyzes the key technologies supporting this architecture. Secondly, based on differentiated service types and communication requirements, an evaluation system for the adaptability between service and communication technology was constructed, and a communication mode adaptation method based on fuzzy analytic hierarchy process (FAHP)-improved CRITIC combined weighting model and grey Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was proposed to achieve the analysis of the adaptability between differentiated service and multi-domain communication technology. According to the analysis of examples, the architecture and adaptation algorithm proposed in this article provide an effective theoretical basis and solution for the selection of multi-domain communication technologies for data center management system service. [ABSTRACT FROM AUTHOR]

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations. [ABSTRACT FROM AUTHOR]

This article discusses the synthesis and characterization of heteroleptic coordination cages using cobalt as the metal center. The authors describe the rational combination of ligands to create three-dimensional cage structures and explore the self-sorting behavior of different ligands in the formation of these cages. They also perform calculations to understand the relative energies of different cage formations. The authors successfully synthesize a target compound and confirm its structure through various analytical techniques. The article provides detailed information on the synthesis and characterization of these cages, highlighting their potential applications in enzymatic catalysis and molecular machines. [Extracted from the article]

This article explores the synthesis and reactivity of germaaluminocene compounds, which are complexes containing germanium and aluminum atoms. The behavior of these compounds is determined by the interaction between the electrophilic aluminum atom and the nucleophilic germanium center. The article describes the synthesis of specific germaaluminocene compounds and their characterization using various analytical techniques. It also discusses the chemical reactions and structures of these compounds, as well as their reactivity with other elements and transition metal complexes. The findings provide valuable insights into the properties and behavior of germaaluminocene compounds. This research was supported by the Deutsche Forschungsgemeinschaft. [Extracted from the article]

This article discusses the metal-free synthesis of trifluoromethyl carbinol-containing imidazo[1,2-a]pyridines through dehydrative coupling. Trifluoromethyl-containing compounds are important in various fields due to their biological activity and stability. Imidazo[1,2-a]pyridines are commonly found in pharmaceuticals, and the construction of CF3-containing imidazo[1,2-a]pyridines is valuable for drug discovery. The use of 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP) as a medium for organic synthesis has been explored, and it has been found to promote the hydroxy-trifluoromethylation of imidazo[1,2-a]pyridines with trifluoroacetaldehyde. The optimized reaction conditions using HFIP as a solvent resulted in good yields of the desired products. The substrate scope of this process was also investigated, demonstrating its generality and practicality. This article describes a novel approach for the synthesis of trifluoromethyl carbinol-containing imidazo[1,2-a]pyridines using imidazo[1,2-a]pyridines and trifluoroacetaldehyde via a dehydrative cross-coupling process. The reaction was promoted by HFIP and exhibited wide substrate generality, excellent functional group tolerance, and mild reaction conditions. [Extracted from the article]

This article discusses the direct synthesis of new azulene derivatives through gold-catalyzed cyclization reactions. The resulting target molecules have potential applications in various fields such as inorganic materials, medicine, and cosmetics. The researchers investigated different reaction conditions and solvents to optimize the yield of azulene, finding that aromatic solvents gave the best yields. They also explored the synthesis of azulene derivatives with thiophene substituents, which showed higher yields. The study provides insights into the synthesis and characterization of these compounds for potential applications in organic materials. The authors also used computational methods to determine the electronic properties of the compounds and suggest further studies on their potential as organic materials. The research was supported by the Deutsche Forschungsgemeinschaft and the Australian Research Council. [Extracted from the article]

Large ensembles of global temperature are provided for three climate scenarios: historical (2006–16), 1.5 °C and 2.0 °C above pre-industrial levels. Each scenario has 700 members (70 simulations per year for ten years) of 6-hourly mean temperatures at a resolution of 0.833° ´ 0.556° (longitude ´ latitude) over the land surface. The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5. The data is stored within the UK Natural and Environmental Research Council Centre for Environmental Data Analysis repository as NetCDF V4 files. [ABSTRACT FROM AUTHOR]

Future communication networks use computing platforms i.e., data centers for enabling content access. The operation of data centers is evolving to meet new requirements such as reducing the operating cost. The use of data centers is recognized to have significant challenges due to high operating costs. The high operating costs arises due to the necessity of data center cooling. The cooling costs can be reduced by siting data centers in the underwater environment. In the underwater environment, data centers are cooled by freely available cold water. However, siting data centers in the underwater environment exposes them to risk from other underwater applications. The use of underwater data centers is susceptible to service loss due to the launch of missiles from submarines in defense applications. Underwater data centers are susceptible to service loss from the launch of missiles from submarines. Hence, it is necessary to design a network architecture that ensures continued service delivery when nuclear attacks occur. The presented research proposes a novel network architecture enabling service continuity in the underwater data center. The proposed architecture incorporates resiliency and comprises terrestrial and non–terrestrial data centers. The proposed network architecture incorporates redundancy and utilizes terrestrial and non-terrestrial data centers. In addition, the research presents a protocol enabling co-existence between underwater data centers and missile launching submarines used in defence applications. The research formulates and evaluates the operational duration, number of packets forwarding paths, and computing resource utilization as the metrics. Performance evaluation shows that the proposed network architecture improves the operational duration and computing resource utilization by an average of (27.7–71.5)% and (23.5–44.2)%, respectively. Furthermore, the proposed network architecture enables the realization of more resilient paths. The use of more resilient paths enhances packet transmission. Evaluation shows that the proposed network architecture enhances the number of resilient packets forwarding paths by (18.2–57.4)% on average. [ABSTRACT FROM AUTHOR]

Exposure to hazardous substances in the workplace can result in injuries and fatalities. This study aimed to investigate the characteristics and trend of occupational exposures reported to the Dutch Poisons Information Centre and to investigate whether the COVID-19 pandemic had an impact on the trend. A retrospective analysis of all acute occupational exposures reported to the Dutch Poisons Information Centre between 1 January 2016 and 31 December 2022 was performed. Data on patient and exposure characteristics, symptoms and treatment recommendations were analyzed. Between 2016 and 2022, the Dutch Poisons Information Centre received 5,508 calls regarding acute occupational exposures. The annual number of calls on acute occupational exposures almost doubled over the years studied (from 475 in 2016 to 936 in 2022). During and after the COVID-19 pandemic (March 2020-December 2022), the number of calls stabilized, but the upward trend was not significantly affected. There were an estimated 0.20 calls per 1,000 human exposure calls per month (95 per cent confidence interval: −0.14; 0.53). Victims were often exposed through multiple routes, with inhalation being the most common route (44 per cent), followed by ocular (32 per cent) and dermal contact (30 per cent). Acids (1,138 exposures) and alkalis (912 exposures) were often involved. The Dutch Poisons Information Centre had information on 6,334 patients, although the total number of exposed patients was not known as some victims did not seek medical assistance, or were treated by healthcare professionals who did not consult our Centre. At the time of contact, 13 per cent (n = 795) of the patients reported no symptoms, 76 per cent (n = 4,805) reported mild to moderate symptoms and 3 per cent (n = 183) reported potentially severe symptoms. Information on symptoms was missing for 9 per cent (n = 551) of the patients. Hospital observation and treatment were recommended for 5 per cent (n = 325) of the patients. This study highlights the necessity for poisoning prevention strategies to reduce the number of work-related incidents involving hazardous substances. The continuing increase in the number of workplace incidents involving hazardous substances is of concern. A comprehensive and multidisciplinary approach should be taken to gain a full understanding of occupational exposure to hazardous substances and to identify risk factors. [ABSTRACT FROM AUTHOR]

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decisionmaking process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multithread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multithread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so. [ABSTRACT FROM AUTHOR]

Background: The Korean Endocrine Hormone Reference Standard Data Center (KEHRS DC) has created reference standards (RSs) for endocrine hormones since 2020. This study is the first of its kind, wherein the KEHRS DC established RSs for serum Cpeptide levels in a healthy Korean population. Methods: Healthy Korean adults were recruited from May 2021 to September 2023. After excluding participants according to our criteria, serum samples were collected; each participant could then choose between fasting glucose only or fasting glucose plus an oral glucose tolerance test (OGTT). If their sample showed high glucose (=100 mg/dL) or hemoglobin A1c (HbA1c) (≥5.70%), their C-peptide levels were excluded from analyzing the RSs. Results: A total of 1,532 participants were recruited; however, only the data of 1,050 participants were analyzed after excluding those whose samples showed hyperglycemia or high HbA1c. Post-30-minute OGTT data from 342 subjects and post-120-minute OGTT data from 351 subjects were used. The means±2 standard deviations and expanded uncertainties of fasting, post-30-minute and 120-minute OGTT C-peptide levels were 1.26±0.82 and 0.34-3.18, 4.74±3.57 and 1.14-8.33, and 4.85±3.58 and 1.25-8.34 ng/mL, respectively. Serum C-peptide levels correlated with obesity, serum glucose levels, and HbA1c levels. Conclusion: The RSs for serum C-peptide levels established in this study are expected to be useful in both clinical and related fields. [ABSTRACT FROM AUTHOR]

This article proposes a novel battery management system (BMS) to ensure uninterruptible power delivery to a 48 V DC bus used for electric vehicle charging stations, data centers, telecommunication systems, and critical care units such as hospitals. The proposed BMS facilitates constant current and constant voltage charging to maintain optimal battery performance during normal operation. This BMS is designed for effective control, monitoring and protection of two lead‐acid battery units to form battery energy storage system (BESS). Furthermore, it is capable of isolating batteries in abnormal conditions and operates them independently to provide reliable supply at output terminals with full capacity. The system utilizes a 30 V DC source derived from AC mains or solar photovoltaic system. This supply is used to charge the BESS and also supply to the load. In the event of failure of 30 V supply, it seamlessly transits to BESS mode to supply power to boost converter to maintain constant 48 V DC output at load terminal. The proposed system architecture not only enhances power reliability but also improves overall system efficiency, making it well‐suited for critical applications require continuous and stable power supply. Simulation studies using Matlab/Simulink and analytical results using TINA (Tool kit for Interactive Network Analysis) are presented to show that 48 V DC supply is maintained at output terminals during failure of input 30 V DC source or failure of one battery unit. [ABSTRACT FROM AUTHOR]

A novel bidirectional passive optical interconnection is proposed for data centers (DCs). The interconnection is based on a coupler of arrayed waveguide grating. The nodes distribute on both sides and simultaneously, bidirectional transmission is allowed. Compared with the traditional passive optical interconnected DCs, the scale is doubled, and the throughput is increased. The crosstalk in simultaneously bidirectional transmission is tested and the results show it is very little. The transmission between the nodes on the same side is relayed by one node on the other side. Some relay methods are proposed, and the relay efficiency is evaluated. [ABSTRACT FROM AUTHOR]

Cloud data centers face various challenges, such as high energy consumption, environmental impact, and quality of service (QoS) requirements. Dynamic virtual machine (VM) consolidation is an effective approach to address these challenges, but it is a complex optimization problem that involves trade-offs between energy efficiency and QoS satisfaction. Moreover, the workload patterns in cloud data centers are often non-stationary and unpredictable, which makes it difficult to model them. In this paper, we propose a new method for dynamic VM consolidation that optimizes both energy efficiency and QoS objectives. Our approach is based on Markov chains and the artificial feeding birds (AFB) algorithm. Markov chains are used to model the resource utilization of each individual VM and PM based on the changes that happen in workload data. AFB algorithm is a metaheuristic optimization technique that mimics the behavior of birds in nature. We modify the AFB algorithm to suit the characteristics of the VM placement problem and to provide QoS-aware and energy-efficient solutions. Our approach also employs an online step detection method to capture variations in workload patterns. Furthermore, we introduce a new policy for VM selection from overloaded hosts, which considers the abrupt changes in the utilization processes of the VMs. The proposed algorithms are evaluated extensively using the CloudSim Toolkit with real workload data. The proposed system outperforms evaluation policies in multiple metrics, including energy consumption, SLA violations, and other essential metrics. [ABSTRACT FROM AUTHOR]

Nowadays, with the rapid expansion of cloud computing technology in processing Internet of Things (IoT) workloads, the demand for data centers has significantly increased, leading to a surge in CO2 emissions, power consumption, and global warming. As a result, extensive research and initiatives have been undertaken to tackle this problem. Two specific approaches focus on enhancing workload scheduling, a complex problem known as NP-Hard, and integrating scheduling into scientific workflows. In this investigation, we present a multi-objective Crow Search Algorithm (CSA) for optimizing both makespan and costs in scientific cloud workflows (CSAMOMC). We conduct a comparative analysis between our approach and the well-known HEFT and TC3pop algorithms, which are commonly used for reducing makespan and optimizing costs. Our findings demonstrate that CSAMOMC is capable of achieving an average makespan reduction of 4.42% and a cost reduction of 4.77% when compared to the aforementioned algorithms. [ABSTRACT FROM AUTHOR]

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints. [ABSTRACT FROM AUTHOR]

Air cooling systems are widely used in current data centers owing to their low capital costs and high reliability. To satisfy the increasing rack power density, the optimal air-cooling technology and an economic analysis should be carefully discussed. Therefore, this study discusses four airflow management technologies: Case 1: raised floor and cold aisle containment supply/computer room air conditioning (CRAC) direct return; Case 2: CRAC direct supply/hot aisle containment (HAC) return; Case 3: overhead duct supply/CRAC direct return; and Case 4: overhead duct supply/HAC return. Using a validated model, the thermal and economic performances of each case were compared. Results showed that Case 4 exhibited the best thermal performance, followed by Cases 3, 2, and 1. Case 1 cannot satisfy the heat dissipation requirement when the rack power density is larger than 12.5 kW; whereas only Case 4 can be used when the power density is larger than 15 kW. Regarding location within China, owing to the high ambient temperature, Shenzhen showed the highest annual cost value and power usage effectiveness, followed by Shanghai, Xi'an, Beijing, and Harbin. Finally, Cases 3 and 4 are recommended for application when the rack power density is greater than 10 kW. [ABSTRACT FROM AUTHOR]

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation. [ABSTRACT FROM AUTHOR]

While cloud computing is crucial in processing data from devices with low computational power, the latency introduced by the Internet backhaul limits real-time applications. By situating computing resources at the network's edge, edge computing offers low-latency services by offloading computations from high-performance computing (HPC) data centers to the edge servers, reducing wide Area network (WAN) strain. As a result, edge computing has unlocked opportunities for innovative applications that were previously unfeasible, such as connected vehicles or medical robotics. Nonetheless, deploying the infrastructure required to support edge computing services raises sustainability and energy consumption concerns. Consequently, the development of tools enabling researchers to explore innovative approaches to reducing the energy impact of edge computing is crucial. In this work, we present MintEDGE, a network simulator focused on the energy consumption of edge computing. Our simulator allows testing energy-saving approaches and task placement algorithms in realistic large-scale scenarios encompassing entire regions. [ABSTRACT FROM AUTHOR]

Venn diagrams serve as invaluable tools for visualizing set relationships due to their ease of interpretation. Widely applied across diverse disciplines such as metabolomics, genomics, transcriptomics, and proteomics, their utility is undeniable. However, the operational complexity has been compounded by the absence of standardized data formats and the need to switch between various platforms for generating different Venn diagrams. To address these challenges, we introduce the EVenn platform, a versatile tool offering a unified interface for efficient data exploration and visualization of diverse Venn diagrams. EVenn (http://www.ehbio.com/test/venn) streamlines the data upload process with a standardized format, enhancing the capabilities for multimodule analysis. This comprehensive protocol outlines various applications of EVenn, featuring representative results of multiple Venn diagrams, data uploads in the centralized data center, and step‐by‐step case demonstrations. Through these functionalities, EVenn emerges as a valuable and user‐friendly tool for the in‐depth exploration of multiomics data. Highlights: Comprehensive Venn functionality: EVenn introduces a unified platform with diverse Venn diagram tools, from interactive diagrams to network representations, catering to the intricate needs of multiomics data analysis across metabolomics, genomics, transcriptomics, and proteomics.Efficient data exploration: EVenn's data center streamlines exploration by supporting a standardized data format, allowing researchers to effortlessly upload and analyze data. The manuscript demonstrates its practical utility through representative results and detailed case demonstrations.User‐friendly interface: With a user‐friendly interface, EVenn simplifies the generation of various Venn diagrams, Euler diagrams, UpSet plots, and more. This protocol establishes EVenn as a valuable resource for researchers seeking a cohesive and accessible tool for multiomics data interpretation. [ABSTRACT FROM AUTHOR]

Geographically distributed cloud data centers (DCs) consume enormous amounts of energy to meet the ever-increasing processing and storage demands of users. The brown energy generated using fossil fuels is expensive and significantly contributes to global warming. Considering the environmental impact caused by the high carbon emissions and relatively high energy cost of brown energy, we propose the integration of renewable energy sources (RES), especially solar and wind energy, with brown energy to power cloud data centers. In our earlier study, we addressed the intermittency of renewable energy sources, where we replaced the random initialization of artificial neural network (ANN) edge weights with the harmony search algorithm (HSA)-optimized assignment of weights. This study incorporated reliably forecast solar and wind energy into the input parameters of our proposed green energy manager (GEM), for cost minimization, carbon emission minimization, and better energy management of cloud DCs, to make our current study more reliable and trustworthy. Four power sources, on-site solar energy and wind energy, off-site solar energy and wind energy, energy stored in energy storage devices, and brown energy, were considered in this study and simulations were carried out for three different cases. The simulation results showed that case 1 (all brown) was 58% more expensive and caused 71% higher carbon emissions than case 2.1 (cost minimization). Case 1 (all brown) was 39% more expensive and had 80% higher carbon emissions than case 2.2 (carbon emission minimization). The simulation results justify the necessity and importance of the GEM, and finally the results proved that our proposed GEM is less expensive and more environmentally friendly. [ABSTRACT FROM AUTHOR]

The increasing use of cloud services in several areas has led to the growth of data-intensive applications. It is necessary to find ways to enhance the efficiency of communications within the networks of data centers to improve the performance of cloud environments. Explicit Congestion Notification (ECN) is used by Data Center TCP (DCTCP) to enhance congestion control in data center networks. The DCTCP uses ECN to assess the amount of congestion, whereas normal TCP congestion management can simply detect its presence. This paper examines DCTCP using the Random Early Detection (RED) queue management strategy. The evaluation reveals that employing Random Early Detection incurs certain costs. The RED is criticized on the one hand for both short- and medium-term connections due to longer completion time delays compared to typical DCTCP techniques. Because of ECN, DCTCP may maintain small queue sizes. However, because RED uses the average queue size, it penalizes short-lived traffic because it does not reach the bottleneck quickly. An intelligent queue management mechanism with ECN is believed to enhance DCTCP's performance in a cloud-computing environment by predicting sending rates and providing fast feedback on queue length. [ABSTRACT FROM AUTHOR]

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data centers. These sensors continuously monitor vital parameters such as energy usage and temperature, thereby providing a comprehensive dataset for analysis. The data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat (NGT) model, enabling a dynamic and accurate representation of the current state of the data center environment. Through the incorporation of the Seagull Optimization Algorithm (SOA), the NGT model optimizes storage migration strategies based on the latest information provided by IoT sensors. The model is trained using 80% of the available dataset and subsequently tested on the remaining 20%. The results demonstrate the effectiveness of the proposed approach, with a Mean Squared Error (MSE) of 5.33% and a Mean Absolute Error (MAE) of 2.83%, accurately estimating power prices and leading to an average reduction of 23.88% in power costs. Furthermore, the integration of IoT data significantly enhances the accuracy of the NGT model, outperforming benchmark algorithms such as DenseNet, Support Vector Machine (SVM), Decision Trees, and AlexNet. The NGT model achieves an impressive accuracy rate of 97.9%, surpassing the rates of 87%, 83%, 80%, and 79%, respectively, for the benchmark algorithms. These findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive capabilities of cloud computing systems. The IoT plays a critical role in driving these advancements by providing real-time data insights into the operational aspects of data centers. [ABSTRACT FROM AUTHOR]

Cloud computing is a dynamic and rapidly evolving field, where the demand for resources fluctuates continuously. This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments. The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently. By adhering to the proposed resource allocation method, we aim to achieve a substantial reduction in energy consumption. This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most, aligning with the broader goal of sustainable and eco-friendly cloud computing systems. To enhance the resource allocation process, we introduce a novel knowledge-based optimization algorithm. In this study, we rigorously evaluate its efficacy by comparing it to existing algorithms, including the Flower Pollination Algorithm (FPA), Spark Lion Whale Optimization (SLWO), and Firefly Algorithm. Our findings reveal that our proposed algorithm, Knowledge Based Flower Pollination Algorithm (KBFPA), consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction. This paper underscores the profound significance of resource allocation in the realm of cloud computing. By addressing the critical issue of adaptability and energy efficiency, it lays the groundwork for a more sustainable future in cloud computing systems. Our contribution to the field lies in the introduction of a new resource allocation strategy, offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures. [ABSTRACT FROM AUTHOR]

One of the major challenges in managing 5G networks is the reconfiguration of network slices. The task covers in particular reconfiguration and relocation of virtual network functions (VNFs) so as to match the service requirements of the slices and the availability of resources of the data centers. In this article, we study in deep the problem of optimal VNFs reconfiguration analyzing a number of its variants. We define two exact, compact integer linear programming formulations of the VNFs reconfiguration problem, and derive two approximate solution algorithms, one of them based on the column generation method. Numerical tests and comparisons illustrate the performance of the algorithms and the quality of the provided solutions. [ABSTRACT FROM AUTHOR]

With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer perceptron (MLP) with Kmeans. This methodology is compared with traditional PL/SQL tools and aims to improve the database response time while outlining future advantages for ML and Kmeans in data processing. We propose a new corollary: h k → H = S S E (C) , w h e r e k > 0 a n d ∃ X , executed on application software querying data collections with more than 306 thousand records. This study produced a comparative table between PL/SQL and MLP-Kmeans based on three hypotheses: line query, group query, and total query. The results show that line query increased to 9 ms, group query increased from 88 to 2460 ms, and total query from 13 to 279 ms. Testing one methodology against the other not only shows the incremental fatigue and time consumption that training brings to database query but also that the complexity of the use of a neural network is capable of producing more precision results than the simple use of PL/SQL instructions, and this will be more important in the future for domain-specific problems. [ABSTRACT FROM AUTHOR]

The antimicrobial resistance of Neisseria gonorrhoeae (NG) is an increasing public health concern, highlighted by the fact that gonococcus is considered as a 'high'-priority pathogen by the WHO for research and development of new therapeutic options. According to the data of the European Centre for Disease Prevention and Control (ECDC) in 2022, the rate of NG infections is the highest recorded since European surveillance of sexually transmitted infections began in 2009. We report a brief description of a patient infected with two different isolates of drug-resistant N. gonorrhoeae. N. gonorrhoeae cultures were positive from oropharyngeal and urethral swabs and isolates had different antimicrobial susceptibility. We investigated the antimicrobial susceptibility of these isolates to six antimicrobials (ceftriaxone, cefixime, azithromycin, ciprofloxacin, tetracycline, and benzylpenicillin), and minimum inhibitory concentrations (MICs; mg/L) were determined using Etest on gonococcal isolates. Oropharyngeal isolate was resistant to azithromycin while urethral was resistant to penicillin, ciprofloxacin, and tetracycline. Two different and phylogenetically distinct sequence types of NG isolates were identified. Understanding the dynamics and drivers of resistance spread can provide an improved rationale for antibiotic management, and the level of NG resistance should be monitored closely. [ABSTRACT FROM AUTHOR]

As research into data centers progresses, the importance of resonant switched-capacitor converters in power supply design becomes more evident. Practical applications reveal that the values of resonant capacitors and inductors may deviate from their nominal values due to various factors, leading to resonant frequency instability. This instability poses a challenge to power electronics technology, affecting system reliability and performance. This paper analyzes the effect of frequency deviation on system functionality, identifies the relationship between output voltage and switching frequency, and proposes a self-tracking frequency strategy to address this issue. Through experimental validation, this approach shows that it maintains synchronization between switching and resonant frequencies, reducing losses associated with frequency misalignment. Simulation and experimental results validate the converter's stable operation and its ability to achieve zero-voltage and zero-current switching. [ABSTRACT FROM AUTHOR]

In response to the construction of green data centers for sustainable development, reducing data center power consumption and carbon dioxide emissions has become an urgent issue. A data center model considering cooling systems was established. The model transforms cost functions of data center power costs, bandwidth costs, carbon emission costs, and cooling costs into a cost minimization problem. The data center operational costs are reduced by effective workload scheduling and rational servers on-off strategies. The electricity prices and carbon emission rates are difficult to predict accurately due to the fluctuation. A feature of the Lyapunov optimization framework was utilized to propose an online control strategy. The strategy only requires knowledge of the current system information. An alternating direction method of multipliers (ADMM) is introduced to enable the control center to coordinate workload distribution among various data centers. Data centers only need to exchange workload decisions, effectively reducing computational complexity and safeguarding user privacy. Simulation results based on real-world data demonstrate that the proposed algorithms can effectively reduce data center operational costs. [ABSTRACT FROM AUTHOR]