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Summary: Disaggregated memory is a promising approach that addresses the limitations of traditional memory architectures by enabling memory to be decoupled from compute nodes and shared across a data center. Cloud platforms have deployed such systems to improve overall system memory utilization, but performance can vary across workloads. High‐performance computing (HPC) is crucial in scientific and engineering applications, where HPC machines also face the issue of underutilized memory. As a result, improving system memory utilization while understanding workload performance is essential for HPC operators. Therefore, learning the potential of a disaggregated memory system before deployment is a critical step. This paper proposes a methodology for exploring the design space of a disaggregated memory system. It incorporates key metrics that affect performance on disaggregated memory systems: memory capacity, local and remote memory access ratio, injection bandwidth, and bisection bandwidth, providing an intuitive approach to guide machine configurations based on technology trends and workload characteristics. We apply our methodology to analyze thirteen diverse workloads, including AI training, data analysis, genomics, protein, fusion, atomic nuclei, and traditional HPC bookends. Our methodology demonstrates the ability to comprehend the potential and pitfalls of a disaggregated memory system and provides motivation for machine configurations. Our results show that eleven of our thirteen applications can leverage injection bandwidth disaggregated memory without affecting performance, while one pays a rack bisection bandwidth penalty and two pay the system‐wide bisection bandwidth penalty. In addition, we also show that intra‐rack memory disaggregation would meet the application's memory requirement and provide enough remote memory bandwidth. [ABSTRACT FROM AUTHOR]

To get beyond the limitations of mobile devices, the mobile cloud is an emerging technology. Offloading resource-intensive applications to distant data centres enabled by the cloud is helping us achieve that. Real-time mobile user applications suffer while using a remote computing solution since MDs encounter increased network response times and delays. In this paper, we suggest an intelligent computational offloading model for MEC. The suggested model aims to apply the DL technique, which automatically chooses the computing source depending on performance, energy consumption, and workload. These factors are used to select the best edge server. For this, a Modified LSTM is proposed in this work. Additionally, TS on the edge cloud infrastructure are directly impacted by VM availability; as a consequence, VM availability is calculated while managing TS. When allocating resources, VM calculations like Make span, task completion times, resource consumption, and migration costs are considered. The capacity to deliver functioning services in the required amount of time after the task is offloaded to the VM is considered while allocating VM resources. Given that this is an optimization problem, the NBUJ hybrid optimization algorithm is developed to solve this issue. At last, the performance of the developed model is validated with the existing models in terms of fitness, migration cost, makespan, resource utilization and so on. And, it is noted that the developed model attains 97.4% of accuracy and 0.0274% of FNR which validates the performance of the proposed model. [ABSTRACT FROM AUTHOR]

Computational chemistry techniques offer tremendous potential for accelerating the discovery of sustainable chemical processes and reactions. However, the environmental impacts of the substantial computing power required for these digital methods are often overlooked. This review provides a comprehensive analysis of the carbon footprint associated with molecular simulations, machine learning, optimization algorithms, and the required data center and research activities within the field of digital chemistry. Successful applications of these methods tackling climate-related issues like CO2 conversion and storage are highlighted, contrasted with assessments of their environmental burden. Strategies to minimize the carbon emissions from computational efforts are evaluated, including sustainable data center practices, efficient coding, reaction optimization, and sustainable research culture. Additionally, we surveyed tools and methodologies for tracking and reporting environmental impacts. Overall, guidelines and best practices are distilled for balancing the green potential of computational chemistry with responsible management of its environmental costs. Assessing and mitigating the field's carbon footprint is crucial for ensuring digital chemical discoveries truly contribute to sustainability goals. [ABSTRACT FROM AUTHOR]

In this paper, a reduced‐switch‐count two‐phase interleaved LLC converter with precise current balancing is proposed by using switching‐control‐capacitor (SCC) technology. Compared with the conventional SCC‐LLC converter, the proposed two‐phase SCC‐LLC converter reduces the switch count with the SCC circuits, and achieves excellent current sharing under all the tolerance conditions between the two‐phase resonant elements. In the proposed converter, the SCC circuit is only used in the first phase. The design of the SCC circuit parameters is analyzed to reduce the voltage stress of the SCC circuit and to ensure that two‐phase currents are balancing with large tolerance of resonant elements. A 2.16‐kW GaN‐based two‐phase interleaved SCC‐LLC converter prototype is established for data center application, and the experimental results show that the 98.1% peak efficiency, the 97.6% full load efficiency, and higher than 96% efficiency at the wide range from 3‐A to 40‐A load current are achieved. The precise current sharing lower than 1% is achieved by using a cheap ceramic capacitor with high tolerance, which validates the performance of the converter. [ABSTRACT FROM AUTHOR]

To mitigate severe physical resource consumption in cloud data centers, we propose an Improved Sparrow Search Algorithm-Based Virtual Machine Placement (ISSA-VMP) method. Incorporating Chebyshev chaotic mapping and Levy flight disturbance enhances resource allocation diversity in the search space. The mapping encoding scheme transforms virtual machine placement solutions into continuous positional information. ISSA-VMP establishes a cloud data center resource consumption model to maximize physical host resource utilization efficiency. The simulation results demonstrate the excellent performance of ISSA-VMP in virtual machine migration and physical resource utilization, significantly reducing task completion time. Compared to the best performing algorithm, the execution rate has increased by 5.57–18.11%. ISSA-VMP achieves high and stable physical resource utilization rates, ensuring efficient utilization, with a stable Service Level Agreement (SLA) violation rate. In summary, ISSA-VMP is a promising, efficient solution for optimizing resource allocation in cloud data centers. [ABSTRACT FROM AUTHOR]

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The cooling of server components has been developed over the past few years in order to meet increasing cooling requirements. The growth in performance and power density increases the cooling demand. To gain a better understanding of the evolution and possible future technology developments in the field of data center cooling, a patent analysis method was used with a focus on the server and computer room levels. After the patent extraction from the European patent database for the period 2000–2023, the search results were classified and analyzed. Most of the patents deal with air or liquid cooling. Since 2015, a technological shift from air to liquid cooling can be identified on the level of patent activities. In conclusion, from the patent analysis, it can be derived that liquid cooling will continue to gain in importance in the future and could also be combined with other approaches in the form of hybrid cooling. However, air cooling may still be relevant, even if the main cooling load is handled by liquid-based approaches. At the same time, the optimization potential for air cooling seems to have been largely exploited in comparison to liquid cooling, as can be seen from the falling number of the patent applications. [ABSTRACT FROM AUTHOR]

Since its first release in 2016, the Cambridge Structural Database Python application programming interface (CSD Python API) has seen steady uptake within the community that the Cambridge Crystallographic Data Centre serves. This article reviews the history of scripting interfaces, demonstrating the need, and then briefly outlines the technical structure of the API. It describes the reach of the CSD Python API, provides a selected review of its impact and gives some illustrative examples of what scientists can do with it. The article concludes with speculation as to how such endeavours will evolve over the next decade. [ABSTRACT FROM AUTHOR]

Erasure coding is extensively deployed in today's data centers to tackle prevalent failures, because it can offer higher reliability at lower storage overhead than data replication. However, for each small write, erasure-coded storage systems have to perform a partial write to an entire erasure coding group, resulting in a time-consuming write-after-read. This paper presents DABRI, an erasure-coded hybrid write approach based on data delta for fast partial writes. DABRI uses data deltas that are the differences between latest data values and original data values, instead of parity deltas to recover the failed data. The data node sends the latest data instead of the parity delta to parity nodes for each partial write. The original data stored on the data node is read and sent to the parity nodes, only when the data stored on the parity nodes is insufficient to maintain the data reliability. This can bypass the computation of parity deltas and reduce the number of data reads. For a series of n partial writes to the same data, DABRI performs log-based updates for data and parity in the first write, performs in-place data updates and log-based parity updates for the last n-1 writes. In addition, the I/O between data nodes and parity nodes is scheduled for parallel I/O in each partial write. We implement an erasure-coded prototype storage system based on DABRI to perform performance evaluation. Experimental results running the real-world traces show that DABRI can significantly improve the I/O throughput, compared with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Today, there is an ever-increasing number of workloads pushed and executed on the Cloud. Data center operators and Cloud providers have embraced application co-location and multi-tenancy as first-class system design concerns to effectively serve and manage these huge computational demands. In addition, the continuous advancements in the computers' hardware technology have made it possible to seamlessly leverage heterogeneous pools of physical machines in data center environments. Even though current modern Cloud schedulers and orchestrators adopt application-aware policies to achieve automation of time-consuming management tasks at scale, e.g., resource provisioning, they still rely on coarse-grained system metrics, such as CPU and/or memory utilization to place incoming applications, thus, not considering (1) interference effects that are provoked by co-located tasks, and (2) the impact on performance caused by the diversity of heterogeneous systems' characteristics. The lack of such knowledge in existing state-of-the-art orchestration solutions results in their inability to perform efficient allocations, which negatively impacts the overall latency distribution delivered by the infrastructure. In this paper, to alleviate this inefficiency, we present a machine learning (ML) based Cloud orchestration extension that takes into account both resource interference and heterogeneity. The framework adequately schedules data-analytics applications on a pool of heterogeneous resources. We evaluate our proposed solution on different application mixes and co-location scenarios. We show that the proposed framework improves the tail latency of the distribution of the deployed applications by up to 3.6x compared to the state-of-the-art Kubernetes scheduler. [ABSTRACT FROM AUTHOR]

Over the past few decades, China emerged as Africa's main partner in digital development. Sino-African cooperation in the Information and Communications Technology (ICT) sector meets African demand for advanced technologies and know-how while helping China to position itself in the global ICT sector. Albeit crucial for Africa's digital development in the short term, this paper argues that the breadth of Sino-African cooperation carries important long-term consequences that could afford China latent control over Africa's digital development. Drawing on Susan Strange's pillars of structural power, this paper considers how the rollout of fiber-optic network cables underpins Chinese control over production, how the construction of data centers might impinge on security, and how research partnerships enhance norms and standards in future innovation—the knowledge pillar. The paper argues that the enabling force of Sino-African cooperation in these three realms is Chinese finance. To test this claim, I conduct a Qualitative Comparative Analysis (QCA) of 42 states in Sub-Saharan Africa, showing that Chinese aid and, to a lesser extent, Chinese loans are the main drivers of Sino-African ties in the ICT sector. The paper concludes that Chinese financial support not only promotes Africa's digital development but also paves the way to Chinese structural power in the ICT sector. [ABSTRACT FROM AUTHOR]

Since today's HPC and data center systems can comprise hundreds of thousands of servers and beyond, it is crucial to equip them with a network that provides high performance. New topologies proposed to achieve such performance need to be evaluated under different traffic conditions, aiming to closely replicate real-world scenarios. While most optimizations should be guided by common traffic patterns, it is essential to ensure that no pathological traffic pattern can compromise the entire system. Determining synthetic adversarial traffic patterns for a network typically relies on a thorough understanding of its topology and routing. In this paper, we address the problem of identifying a generic adversarial traffic pattern for low-diameter direct interconnection networks. We first focus on Random Regular Graphs (RRGs), which represent a typical case for these networks. Moreover, RRGs have been proposed as topologies for interconnection networks due to their superior scalability and expandability, among other advantages. We introduce Ant Mill, an adversarial traffic pattern for RRGs when using routes of minimal length. Secondly, we demonstrate that the Ant Mill traffic pattern is also adversarial in other low-diameter direct interconnection networks such as Slimfly, Dragonfly, and Projective networks. Ant Mill is thoroughly motivated and evaluated, enabling future studies of low-diameter direct interconnection networks to leverage its findings. [ABSTRACT FROM AUTHOR]

Based on the differential idea, this article establishes two segmented algorithms which are the linear equations method and iterative method for solving the one-dimensional digital twin of the temperature field and pressure field of plate heat exchanger (PHE). The segmented algorithms are to consider the change of the physical properties of the fluid along the flow direction, which makes the solution of the temperature field and pressure field more accurate than that without segmentation. It can still get other important parameters besides temperature and is easy to converge with a few iterative steps. When the boundary conditions are known, it can accurately solve the heat transfer, wall temperature, convective heat transfer coefficient, temperature field and pressure field of fluid. Under six groups of test conditions, the heat exchange, temperature and pressure calculated by the two algorithms are compared with the commercial software Flownex, when the number of segments is 100, the average maximum error of each segment is not more than −0.39%, −0.97%, and 0.18%, respectively. To balance accuracy and calculation speed, this article also explores the impact of the number of segments on accuracy. It is found that when the number of segments increases to 10, the impact on the calculation accuracy can be ignored. Compared with Flownex, these two algorithms have smaller errors, and the solution speed of solving linear equations method is faster than that of iteration. It provides application value for emerging digital twin technology, data center chilled water system and other projects and industries that need to accurately control the outlet temperature and wall temperature of PHE. [ABSTRACT FROM AUTHOR]

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable. [ABSTRACT FROM AUTHOR]

Background: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability. Purpose: To develop a deep learning‐based method for automatic septum segmentation from black‐blood MR images for the myocardial T2* measurement of thalassemia patients. Study Type: Retrospective. Population/Subjects: One hundred forty‐six transfusion‐dependent thalassemia patients with cardiac MR examinations from two centers. Data from Center 1 (1.5 T) were assigned to the training (100 examinations) and internal testing (20 examinations) sets; data from Center 2 were assigned to the external testing set (26 examinations; 10 at 1.5 T and 16 at 3.0 T). Field Strength/Sequence: 1.5 T and 3.0 T, multiecho gradient‐echo sequence. Assessment: A modified attention U‐Net for septum segmentation was constructed and trained, and its performance evaluated on unseen internal and external datasets. T2* was measured by fitting the average septum signal, separately segmented by automatic and manual methods. Statistical Tests: Agreement between manual and automatic septum segmentations was assessed with the Dice coefficient, and T2* agreement was assessed using the Bland–Altman plot and the coefficient of variation (CoV). Results: The median Dice coefficient of deep network‐based septum segmentation was 0.90 [0.05] on the internal dataset, 0.82 [0.10] on the external 1.5 T dataset, and 0.86 [0.14] on the external 3.0 T dataset. T2* measurements using automatic segmentation corresponded with those from manual segmentation, with a mean difference of 0.02 (95% LoA: −0.74 to 0.79) msec, 0.43 (95% LoA: −2.1 to 3.0) msec, and 0.36 (95% LoA: −0.72 to 1.4) msec on the three datasets. The CoVs between the two methods were 3.1%, 7.0%, and 6.1% on the internal and two external datasets, respectively. Data Conclusions: The proposed septum segmentation yielded myocardial T2* measurements which were highly consistent with those obtained by manual segmentation. This automatic approach may facilitate data processing and avoid operator‐dependent variability in practice. Evidence Level: 4 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

With technological advancements, much data is being captured by sensors, smartphones, wearable devices, and so forth. These vast datasets are stored in data centres and utilized to forge data‐driven models for the condition monitoring of infrastructures and systems through future data mining tasks. However, these datasets often surpass the processing capabilities of traditional information systems and methodologies due to their significant size. Additionally, not all samples within these datasets contribute valuable information during the model training phase, leading to inefficiencies. The processing and training of Machine Learning algorithms become time‐consuming, and storing all the data demands excessive space, contributing to the Big Data challenge. In this paper, we propose two novel techniques to reduce large time‐series datasets into more compact versions without undermining the predictive performance of the resulting models. These methods also aim to decrease the time required for training the models and the storage space needed for the condensed datasets. We evaluated our techniques on five public datasets, employing three Machine Learning algorithms: Holt‐Winters, SARIMA, and LSTM. The outcomes indicate that for most of the datasets examined, our techniques maintain, and in several instances enhance, the forecasting accuracy of the models. Moreover, we significantly reduced the time required to train the Machine Learning algorithms employed. [ABSTRACT FROM AUTHOR]

In monitoring transmission line external damage prevention, due to the limited memory computing power of the equipment, the image needs to be transmitted to the data center at regular intervals, resulting in a high false negative rate. Therefore, this paper proposes a target detection method based on lightweight YOLOv5s. First, DSConv and improved E‐ELAN are used in Backbone to reduce the model's parameters. Then, GSConv and VoV‐GSCSP are introduced in Neck to reduce the complexity of the model. Finally, the Mish activation function achieves more effective feature transfer. According to the experimental findings, the proposed model's parameters are about 37% smaller than the original model's, and the calculation amount is about 53% smaller. The detection accuracy on the self‐built data set is the same, which proves that the proposed algorithm can reduce the model while maintaining high detection performance. It has specific practical significance for the terminal real‐time detection of external mechanical damage targets. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Deep learning has revolutionized many sectors of industry and daily life, but as application scale increases, performing training and inference with large models on massive datasets is increasingly unsustainable on existing hardware. Highly parallelized hardware like Graphics Processing Units (GPUs) are now widely used to improve speed over conventional Central Processing Units (CPUs). However, Complementary Metal-oxide Semiconductor (CMOS) devices suffer from fundamental limitations relying on metallic interconnects which impose inherent constraints on bandwidth, latency, and energy efficiency. Indeed, by 2026, the projected global electricity consumption of data centers fueled by CMOS chips is expected to increase by an amount equivalent to the annual usage of an additional European country. Silicon Photonics (SiPh) devices are emerging as a promising energy-efficient CMOS-compatible alternative to electronic deep learning accelerators, using light to compute as well as communicate. In this review, we examine the prospects of photonic computing as an emerging solution for acceleration in deep learning applications. We present an overview of the photonic computing landscape, then focus in detail on SiPh integrated circuit (PIC) accelerators designed for different neural network models and applications deep learning. We categorize different devices based on their use cases and operating principles to assess relative strengths, present open challenges, and identify new directions for further research. [ABSTRACT FROM AUTHOR]

Recent years, the tremendous number of distributed energy resources, electric vehicles are integrated into the Low-Voltage Distribution Network (LVDN), large amount of data are generated by edge devices in LVDN. The cloud data centers are unable to process these data timely and accurately, making it impossible to meet the demand for fine-grained control of LVDN. To solve the above problems, this paper proposes a flexible orchestration of lightweight artificial intelligence (AI) for edge computing in LVDN. Firstly, the application requirements of LVDN are analysed through feature extraction of its historical data, and a lightweight AI library is constructed to meet its requirements. Secondly, based on the multifactor priority, a flexible orchestration model is established, to allow the lightweight AI embedded in the edge devices of the LVDN. Finally, the particle swarm optimization algorithm is used to provide the best solution. The simulation results show that the method proposed in this paper can support the deployment of AI at the edge. It can significantly improve the utilization of edge computing resources, and reduce the pressure of cloud computing and the time of application processes. [ABSTRACT FROM AUTHOR]

The HALO–(AC)3 aircraft campaign was carried out in March and April 2022 over the Norwegian and Greenland Seas, the Fram Strait, and the central Arctic Ocean. Three research aircraft, the High Altitude and Long Range Research Aircraft (HALO), Polar 5, and Polar 6, performed 54 partly coordinated research flights on 23 flight days over areas of open ocean, the marginal sea ice zone (MIZ), and the central Arctic sea ice. The general objective of the research flights was to quantify the evolution of air mass properties during moist and warm air intrusions (WAIs) and cold air outbreaks (CAOs). To gain a comprehensive data set, the three aircraft followed different strategies. HALO was equipped with active and passive remote sensing instruments and dropsondes to cover the regional evolution of cloud and thermodynamic processes. Polar 5 carried a similar remote sensing payload as HALO, and Polar 6 was instrumented with in-situ cloud, aerosol, and trace gas instruments focusing on the initial air mass transformation close to the MIZ. The processed, calibrated, and validated data are published in the World Data Center PANGAEA as instrument-separated data subsets and listed in aircraft-separated collections for HALO (Ehrlich et al., 2024a), Polar 5 (Mech et al., 2024a), and Polar 6 (Herber et al., 2024). A detailed overview of the available data sets is provided here. Furthermore, the campaign-specific instrument setup, the data processing, and data quality are summarized. Based on measurements conducted during a specific CAO, it is shown that the scientific analysis of the HALO– (AC)3 data benefits from the coordinated operation of the three aircraft. [ABSTRACT FROM AUTHOR]

This article discusses the synthesis of a new P-chiral phospholene core-based ligand called LemPhos. The authors propose a method using a chiral diene to create diastereomers that are easier to separate, allowing for the production of P-chiral ligands. The ligands are synthesized and characterized, and their application as catalysts in asymmetric hydrogenation reactions is explored. The document also includes experimental procedures and spectroscopic data for the synthesized compounds. The research is dedicated to the memory of Stanisław Lem, a renowned science-fiction writer. [Extracted from the article]

With the continuous expansion of network scale, fault tolerance and security in network operation have become increasingly important. The high vertex connectivity of the interconnection network indicates excellent fault tolerance. In order to address the drawbacks of traditional connectivity, the g-extra connectivity has been proposed to better reflect the fault tolerance of the network. RRect is a newly proposed server-centered data center network with good interconnection structure, and it has excellent flexibility and energy efficiency. In this paper, we focus on the logical structure of RRect, denoted by R R n , m , k . Firstly, we investigate the extra connectivity of R R n , m , k , and, in detail, we prove that for n ≥ 4 , m ≥ 1 , and k ≥ 4 , the { 1 , 2 , 3 } -extra connectivity of R R n , m , k is 2 k m (n - 1) + m n - 2 , 3 k m (n - 1) + m n - 3 , and 4 k m (n - 1) - 4 m , respectively, which are about 2, 3, and 4 times of its traditional connectivity. Secondly, we conduct numerical simulation experiments on the extra connectivity of R R n , m , k and evaluate the experimental performance. The result can provide a more comprehensive measurement of its reliability. [ABSTRACT FROM AUTHOR]

The distributed energy power system needs to provide sufficient and flexible computing power on demand to meet the increasing digitization and intelligence requirements of the smart grid. However, the current distribution of the computing power and loads in the energy system is unbalanced, with data center loads continuously increasing, while there is a large amount of idle computing power at the edge. Meanwhile, there are a large number of real-time computing tasks in the distributed energy power system, which have strict requirements on execution deadlines and require reasonable scheduling of multi-level heterogeneous computing power to meet real-time computing demands. Based on the aforementioned background and issues, this paper studies the real-time service scheduling problem in a multi-level heterogeneous computing network of distributed energy power systems. Specifically, we consider the divisibility of tasks in the model. This paper presents a hierarchical real-time task-scheduling framework specifically designed for distributed energy power systems. The framework utilizes an orchestrating agent (OA) as the execution environment for the scheduling module. Building on this, we propose a hierarchical selection algorithm for choosing the appropriate network layer for real-time tasks. Further, we develop two scheduling algorithms based on greedy strategy and genetic algorithm, respectively, to effectively schedule tasks. Experiments show that the proposed algorithms have a superior success rate in scheduling compared to other current algorithms. [ABSTRACT FROM AUTHOR]

Summary: Software‐defined network (SDN) technology is widely used for computer networks, especially in enterprise data centers and virtualized networking. However, SDN networks encounter severe challenges to security. One such challenge comes from third‐party applications that contain malicious logic and security vulnerabilities, resulting in controller integrity attacks. In this paper, we propose a defensive mechanism of object integrity for SDN (DMoiSDN) to mitigate the issue known as Cross‐App Poisoning (CAP). Our results contribute to increasing the integrity level of the controller's resources by conducting a potential risk analysis, which showed a decrease of 57% in the risk factor for potential attacks. We further examined the results of comparing DMoiSDN's performance with related work that uses information flow control (IFC) policies. The best results among the three conducted scenarios were as follows: we found that decreased latency in our system ranged from 12% to 90%, with an average of 59%, when encountering an increase in requests. It ranged from 78% to 49%, with an average of 63%, when receiving a variable number of total permissions for each application. DMoiSDN is expected to show a perceptible but reasonable latency and, to some extent, be able to avoid a critical impact on performance. [ABSTRACT FROM AUTHOR]

With the rapid growth of the digital economy, data centers have emerged as significant consumers of electricity. This presents challenges due to their high energy demand but also brings opportunities for utilizing waste heat. This paper introduces an operation optimization method for multi-energy systems with data centers, leveraging the information gap decision theory (IGDT) to consider various uncertainties from data requests and the environment. First, a model is established for the operation of a multi-energy system within data centers, considering the integration of server waste heat recovery technology. Second, IGDT is employed to address uncertainties of photovoltaic output and data load requests, thereby formulating an optimal energy management strategy for the data center park. Case studies demonstrate that the electricity purchase cost increased by 5.3%, but the total cost decreased by 30.4%, amounting to 5.17 thousand USD after optimization. It indicates that the operational strategy effectively ensures both efficient and cost-effective power supply for the data center and the park. Moreover, it successfully mitigates the risks associated with fluctuations in data load, thus minimizing the possibility of data load abandonment during uncertain periods. [ABSTRACT FROM AUTHOR]

The Virtual Atomic and Molecular Data Centre (VAMDC) is a distributed infrastructure combining heterogeneous atomic and molecular (A &M) data from more than 41 autonomous databases. In this paper, we present the Species Database, a scientific web service which is a central catalogue providing a useful information on A &M species from all the VAMDC databases. The Species Database is one the key pillars in the technical architecture of the VAMDC, and from the user's point of view, it is the service to simply and immediately answer the question: Does VAMDC have data on my molecule/atom of interest? This work is structured as follows: After recalling the aims and needs that led to the construction of the Species Database, we will describe how it works, with increasing levels of complexity. First, we will describe how any user, even those with no knowledge of VAMDC, can easily use the dedicated web interface to search for and extract information. Then we will describe an API that more advanced users can use to interact with the Species Database in a more programmatic way. Two appendices cover some technical aspects that are not necessary for using the Species Database, but which may be of interest to readers wishing to understand implementation details and low-level technical subtleties. [ABSTRACT FROM AUTHOR]

Natural resources and geographic information big data are regarded as essential productive factors in the context of digital government, and an important component of the national integrated government big data system. Due to data dispersion and isolated application program existing in the various departments, it is difficult to realize data sharing and application across departments and businesses, leading to the low utilization rate of data. In response to the issues mentioned above, the digital government spatio-temporal data center was designed with the demands for natural resource and geographic information big data services. The critical components, including storage computation, data structure and application support were introduced clearly. By integrating the natural resources and geographic information database with the population comprehensive database and legal person comprehensive database, an organic composition of people, enterprises and geographic locations was established, which was explained by the specific application practices. [ABSTRACT FROM AUTHOR]

The energy consumption of Cloud–Edge systems is becoming a critical concern economically, environmentally, and societally; some studies suggest data centers and networks will collectively consume 18% of global electrical power by 2030. New methods are needed to mitigate this consumption, e.g. energy-aware workload scheduling, improved usage of renewable energy sources, etc. These schemes need to understand the interaction between energy considerations and Cloud–Edge components. Model-based approaches are an effective way to do this; however, current theoretical Cloud–Edge models are limited, and few consider energy factors. This paper analyses all relevant models proposed between 2016 and 2023, discovers key omissions, and identifies the major energy considerations that need to be addressed for Green Cloud–Edge systems (including interaction with energy providers). We investigate how these can be integrated into existing and aggregated models, and conclude with the high-level architecture of our proposed solution to integrate energy and Cloud–Edge models together. [ABSTRACT FROM AUTHOR]

Server energy consumption constitutes a significant portion of the overall energy usage in cloud data centers. Achieving energy optimization through tuning server parameter configurations is of paramount importance for energy conservation within cloud data centers. However, due to the diverse and dynamic nature of mixed workloads in cloud servers, achieving highly energy efficient server parameter configurations often necessitates continuous adjustments and optimizations in response to workload fluctuations. To tackle this challenge, this paper introduces an energy-efficient optimization method for cloud servers tailored to scenarios with mixed workloads, known as Energy-aware Parameter Tuning for Mixed Workloads (EPTMW). This method dynamically performs joint tuning of CPU frequency and system kernel parameters based on the operational status of the real-time workloads. Additionally, we evaluate EPTMW using benchmark workloads from BenchSEE and SERT. The experimental results demonstrate that EPTMW can enhance energy efficiency by 28.5 % compared to the energy efficiency level achieved under the default server parameter configuration, all while maintaining server performance. [ABSTRACT FROM AUTHOR]

In the modern era, large-scale computing systems distribute tasks into smaller units, allowing them to be executed simultaneously, accelerating job completion, and reducing energy usage. However, cloud computing systems face a significant challenge: the Long Tail problem. This problem arises when a small subset of slow-performing tasks impedes the overall progress of parallel job execution, resulting in longer service response times and decreased system efficiency. To reduce task execution time and energy consumption, we propose an efficient straggler task management framework for cloud data centers in this paper. A neural network-based resource predictor is initially developed and tuned with the Stochastic Gradient Descent with Momentum mechanism to analyze and classify heterogeneous tasks into stragglers and non-stragglers. Then, after identifying the straggler tasks, they are further classified into two categories: Resource Hunters and Long-Tail stragglers, based on their specific resource requirements. A task management policy is implemented to achieve parallelism and enhance sustainability in the cloud infrastructure. Considering the task category, this policy effectively schedules and allocates resources among user job requests. To evaluate the effectiveness of the proposed work, extensive simulations are performed using the Google Cluster Dataset (GCD). The results obtained from these simulations are subsequently compared to state-of-the-art techniques for a comprehensive analysis. The experimental results reveal substantial improvements in various metrics, including power consumption and active servers showing reductions of up to 55.16% and 35%, respectively. Furthermore, there has been a reduction in execution time of up to 67.74%. [ABSTRACT FROM AUTHOR]

Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement (VMP) involves assigning VMs across various physical machines, which is a crucial process impacting energy draw and resource usage in the cloud data center. Nonetheless, finding an effective settlement is challenging owing to factors like hardware heterogeneity and the scalability of cloud data centers. This paper proposes an efficient algorithm named VMP-ER aimed at optimizing power consumption and reducing resource wastage. Our algorithm achieves this by decreasing the number of running physical machines, and it gives priority to energy-efficient servers. Additionally, it improves resource utilization across physical machines, thus minimizing wastage and ensuring balanced resource allocation. [ABSTRACT FROM AUTHOR]

The physical river characteristics datasets described here provide spatially consistent information to support hydrological and inundation modelling at a 1 km × 1 km resolution across the United Kingdom, on the British National Grid. The datasets of physical river characteristics provide gridded datasets (outflow drainage directions, catchment areas, widths of bankfull rivers and depths of bankfull rivers) and a comma‐separated table of NRFA (National River Flow Archive) gauging station locations. These datasets are derived from a range of sources: outflow drainage directions, catchment area and bankfull river widths are derived from existing higher resolution datasets, whereas bankfull river depths were harder to source and instead are derived from sparse historical measurements. The new gridded datasets provide a derived value for each UK land cell on the British National Grid (BNG). The comma‐separated NRFA gauging station locations table provides the most appropriate locations of 1,499 river flow gauging stations on the 1 km resolution grids, together with the approximate error in the 1 km × 1 km gridded delineation of the upstream catchment area. This article explains how UK‐wide 1‐km grids of these variables were estimated, their format and how to use them. The data are available from the Environmental Information Data Centre (EIDC). [ABSTRACT FROM AUTHOR]

We estimated the population-level incidence of human papillomavirus (HPV)–positive oropharyngeal, cervical, and anal cancers by smoking status. We combined HPV DNA genotyping data from the Centers for Disease Control and Prevention's Cancer Registry Sentinel Surveillance System with data from the Kentucky Cancer Registry and Behavioral Risk Factor Surveillance System across smoking status. During 2004-2005 and 2014-2015 in Kentucky, most cases of oropharyngeal (63.3%), anal (59.7%), and cervical (54.9%) cancer were among individuals who ever smoked. The population-level incidence rate was higher among individuals who ever smoked than among those who never smoked for HPV-positive oropharyngeal (7.8 vs 2.1; adjusted incidence rate ratio = 2.6), cervical (13.7 vs 6.8; adjusted incidence rate ratio = 2.0), and anal (3.9 vs 1.6; adjusted incidence rate ratio = 2.5) cancers. These findings indicate that smoking is associated with increased risk of HPV-positive oropharyngeal, cervical, and anal cancers, and the population-level burden of these cancers is higher among individuals who ever smoked. [ABSTRACT FROM AUTHOR]

The early diagnosis and treatment of foreign body aspiration (FBA) can significantly improve the overall prognosis of children. There are significant differences in the epidemiology and clinical characteristics of FBA in different regions. Therefore, we conducted a real-world study in the western region of China with over 4000 patients. The aim of this study was to improve the understanding of FBA in terms of its types, the specific months of its occurrence, and the distribution of primary caregiver characteristics in western China. We collected the clinical and epidemiological data of children who were diagnosed with FBA in our hospital over the past 20 years through a big data centre. We matched the data of healthy children who underwent routine physical examinations at the paediatric health clinic during the same period to analyse the differences in the data of actual guardians. A total of 4227 patients from five provinces were included in this study. Foreign bodies were removed by rigid bronchoscopy in 99.4% (4202/4227) of patients, with a median age of 19 months and a median surgical duration 16 min. January was the most common month of onset for 1725 patients, followed by February, with 1027 patients. The most common types of foreign objects were melon peanuts, seeds and walnuts, accounting for 47.2%, 15.3%, and 10.2%, respectively. In the FBA group, the proportion of grandparents who were primary caregivers was 70.33% (2973/4227), which was significantly greater than the 63.05% in the healthy group (2665/4227) (P < 0.01). FBA most commonly occurs in January and February. More than 60% of FBAs occur between the ages of 1 and 2 years, and the incidence of FBA may be greater in children who are cared for by grandparents. A rigid bronchoscope can be used to remove most aspirated foreign bodies in a median of 16 min. [ABSTRACT FROM AUTHOR]

A web search, an online video, a connected Nest device, and hundreds of cloud services all give us a response in a fraction of a second. But what really happens when we click search or send a request. The request travels over the public internet and into fiber network. Millions of requests or packets of data travel through miles of cable over land and under sea, converging at one of the many data centers that operate all over the world. Data Center (DC) is the core site of data operation, storage and forwarding, which is also an important part of cloud platform. A large number of commercial switches and servers are usually deployed at the DC. The DC is a complex set of facilities. Data center networks (DCN) is a network applied in the DC, because the traffic in DC presents the typical characteristics of centralized exchange data and increased traffic, which puts forward further requirements for the DCN. DCN connects a large-scale server cluster and is a bridge for data transmission and storage. With the expansion of DC and the increasing number of service types, communication within data centers becomes more frequent. On the other hand, traffic between data centers has also increased dramatically. Considering the multi-layered transmission mode and traffic characteristics of DCN, this paper proposes a software defined networking (SDN) -based multi-layered traffic scheduling scheme for DCN, which is mainly focused on hop count, criticality and cost. Moreover, based on SDN architecture and Deep Q-Network of Reinforcement Learning (RL), the intelligent multi-layered traffic scheduling scheme is proposed to obtain the current optimal global routing strategy according to the real-time traffic demand in the network. The simulation results show that the proposed scheme outperforms benchmarks in terms of average throughput, normalized total throughput, link bandwidth utilization, average round-trip time and network traffic bandwidth loss rate. [ABSTRACT FROM AUTHOR]

With the increasing enrollment of higher education in China, teaching quality evaluation is an urgent problem to be studied. Colleges and universities should also have their own set of teaching quality evaluation system with the evaluation activities, and thus pre-evaluate their teaching and ensure the teaching quality between the two teaching evaluations. At present, big data is more and more widely used, and distributing these big data to corresponding scores through content centric data center networks (CCDCNs) provides Quality of Experience (QoE) for English teaching quality assessment. Therefore, one of the challenges faced by the current network is to enhance QoE under content distribution. In this work, in order to solve this problem, we schedule the cached teaching feature vector to the corresponding score level. Three cache scheduling algorithms are constructed in CCDCNs. Firstly, an approximate dynamic algorithm is proposed, which has high complexity. Then, based on the characteristics of node centralization, we propose an improved approximate dynamic scheduling algorithm. Although the algorithm includes the scheduling of cached content and the scheduling of content transmission rate, it has low complexity in processing scheduling. In addition, we propose a cache scheduling algorithm based on deep reinforcement learning (DRL). Although the algorithm has high complexity, the scheduling accuracy is also high. Experiments show that the method proposed in this work can obtain higher QoE, and excellent performance in English teaching evaluation, about 7.8% improvement degree. [ABSTRACT FROM AUTHOR]

To face with the surge of cloud computing services, energy problem and carbon mission constraint, more and more geo-distributed hybrid powered data centers are build over the world. Geo-distributed hybrid powered data centers are located in different locations equipped with different types of green energy source. In this work, we propose a periodic requests dispatcher, including a periodic model and a spatial balance algorithm, to minimize the brown energy use. The periodic model divides the long-term period into several time slots, and the spatial balance algorithm achieves energy optimization by balancing user requests in each time slot. Besides, a research case is simulated by Cloudsim platform. We conduct several comparative analysis in aspect of energy utilization, electricity cost and carbon emission. The experiment results show that the dispatcher can effectively reduce the electricity cost, and reasonably achieve the request distribution balance in geo-distributed hybrid powered data centers. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Is there an association between the length of in vitro culture, mode of ART and the initial endogenous hCG rise, in cycles with a foetal heartbeat after single embryo transfer (ET) and implantation? SUMMARY ANSWER Both the length of in vitro culture and the mode of ART have an impact on the initial endogenous rise in hCG in singleton pregnancies. WHAT IS KNOWN ALREADY Different factors have been identified to alter the kinetics of hCG in pregnancies. Current studies show conflicting results regarding the kinetics of hCG after different types of ART (fresh vs frozen ET (FET)), the inclusion or not of preimplantation genetic testing (PGT), and the length of time in in vitro culture. STUDY DESIGN, SIZE, DURATION This was a multicentre cohort study, using prospectively collected data derived from 4938 women (5524 treatment cycles) undergoing IUI (cycles, n = 608) or ART (cycles, n = 4916) treatments, resulting a in singleton ongoing pregnancy verified by first-trimester ultrasound scan. Data were collected from the Danish Medical Data Centre, used by the three participating Danish public fertility clinics at Copenhagen University hospitals: Herlev Hospital, Hvidovre Hospital, and Rigshospitalet, from January 2014 to December 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS The fresh ET cycles included cleavage-stage (2 or 3 days in vitro) and blastocyst (5 days in vitro) transfers. FET cycles included cleavage-stage (3 days in vitro before cryopreservation) or blastocyst (5 or 6 days in vitro before cryopreservation) transfers. The IUI cycles represented no time in vitro. To attain a comparable interval for serum-hCG (s-hCG), the ovulation induction time was identical: 35–37 h before oocyte retrieval or IUI. The conception day was considered as: the insemination day for pregnancies conceived after IUI, the oocyte retrieval day for fresh ET, or the transfer day minus 3 or 5 as appropriate for FET of Day 3 or 5 embryos. Multiple linear regression analysis was used, including days post-conception for the hCG measurement as a covariate, and was adjusted for the women's age, the cause of infertility, and the centre. For FET, a sensitivity analysis was used to adjust for endometrial preparation. MAIN RESULTS AND THE ROLE OF CHANCE The study totally includes 5524 cycles: 2395 FET cycles, 2521 fresh ET cycles, and 608 IUI cycles. Regarding the length of in vitro culture, with IUI as reference (for no time in in vitro culture), we found a significantly lower s-hCG in pregnancies achieved after fresh ET (cleavage-stage ET or blastocyst transfer). S-hCG was 18% (95% CI: 13–23%, P  < 0.001) lower after fresh cleavage-stage ET, and 23% (95% CI: 18–28%, P  < 0.001) lower after fresh blastocyst transfer compared to IUI. In FET cycles, s-hCG was significantly higher after blastocyst transfers compared to cleavage-stage FET, respectively, 26% (95% CI: 13–40%, P  < 0.001) higher when cryopreserved on in vitro Day 5, and 14% (95% CI: 2–26%, P  = 0.02) higher when cryopreserved on in vitro Day 6 as compared to Day 3. Regarding the ART treatment type, s-hCG after FET blastocyst transfer (Day 5 blastocysts) cycles was significantly higher, 33% (95% CI: 27–45%, P  < 0.001), compared to fresh ET (Day 5 blastocyst), while there was no difference between cleavage-stage FET (Days 2 + 3) and fresh ET (Days 2 + 3). S-hCG was 12% (95% CI: 4–19%, 0.005) lower in PGT FET (Day 5 blastocysts) cycles as compared to FET cycles without PGT (Day 5 blastocysts). LIMITATIONS, REASONS FOR CAUTION The retrospective design is a limitation which introduces the risk of possible bias and confounders such as embryo score, parity, and ovarian stimulation. WIDER IMPLICATIONS OF THE FINDINGS This study elucidates how practices in medically assisted reproduction treatment are associated with the hCG kinetics, underlining a potential impact of in vitro culture length and mode of ART on the very early embryo development and implantation. The study provides clinicians knowledge that the type of ART used may be relevant to take into account when evaluating s-hCG for the prognosis of the pregnancy. STUDY FUNDING/COMPETING INTEREST(S) No funding was received for this study. AP has received consulting fees, research grants, or honoraria from the following companies: Preglem, Novo Nordisk, Ferring Pharmaceuticals, Gedeon Richter, Cryos, Merck A/S, and Organon. AZ has received grants and honoraria from Gedeon Richter. NLF has received grants from Gedeon Richter, Merck A/S, and Cryos. MLG has received honoraria fees or research grants from Gedeon Richter, Merck A/S, and Cooper Surgical. CB has received honoraria from Merck A/S. MB has received research grants and honoraria from IBSA. MPR, KM, and PVS all report no conflicts of interest. TRIAL REGISTRATION NUMBER The study was registered and approved by the Danish Protection Agency, Capital Region, Denmark (Journal-nr.: 21019857). No approval was required from the regional ethics committee according to Danish law. [ABSTRACT FROM AUTHOR]

Edge computing serves as a critical intermediary for secure data exchange between IoT devices and data centres in healthcare, where the protection of sensitive patient data is paramount. This study presents UdesMec, a comprehensive edge computing framework designed for efficient and secure data storage and processing in such contexts. Focusing on the challenge of implementing robust data security models in the presence of limited resources at edge nodes, UdesMec employs a unified encryption approach that is particularly suited for expert knowledge-based prediction of complex critical medical events. Using secret sharing and homomorphic encryption, it ensures the confidentiality and integrity of sensitive patient data transmitted by IoT devices, while enabling efficient computation of prediction algorithms on a cloud server. The experimental evaluation confirms the strong encryption performance and the ease of use of UdesMec, positioning it as a promising solution for the secure and reliable prediction of complex critical medical events in healthcare systems based on edge computing. [ABSTRACT FROM AUTHOR]

This paper examines the use of an Uninterruptible Power Supply (UPS) to enhance the operational efficiency of data centers. It focuses on developing an optimal energy scheduling strategy for a data center equipped with UPS, using the Markov decision process (MDP) framework. The MDP framework simulates the decision-making process involved in minimizing energy costs. Each unit's available power output in the data center is treated as a Markov state, taking into account the uncertainty associated with renewable distributed generation. This uncertainty drives the system to transition to other Markov states in subsequent decision times. A recursive optimization model is established for each Markov state at each decision time to guide state-based operations, which includes determining the unit output while considering both current and future costs. The challenge of dealing with high dimensionality, arising from a substantial number of states and actions in the model, is effectively addressed by adopting an approximate dynamic programming (ADP) method. This approach incorporates decision-state and forwards dynamic algorithms to tackle the complexity of the MDP-based model. By employing ADP, the computational burden is reduced, enabling efficient and practical solutions to be obtained. [ABSTRACT FROM AUTHOR]

This article discusses the synthesis and characterization of a thermally stable monoruthenium acetylide radical species. The researchers examined the reactivity and properties of metal acetylide complexes, specifically focusing on mononuclear ruthenium acetylide radicals. They found that the choice of metal center and ligands significantly influenced the reactivity of these complexes. The researchers were able to isolate and characterize a mononuclear ruthenium acetylide radical species, which exhibited high thermal stability. This study provides insights into the design strategy for controlling the reactivity of radicals in metal complexes. [Extracted from the article]

This article, published in the journal Synlett, discusses a study on the development of a rhodium-catalyzed direct vinylene annulation reaction. The researchers focused on using vinyl selenones as vinylene-transfer reagents and successfully demonstrated a three-component reaction to construct isoquinolone scaffolds. The article includes a table showing the optimization of reaction conditions. The study also explored the scope and limitations of the reaction system, proposing a mechanism for the coupling reaction and concluding that further optimization is needed. [Extracted from the article]

With the growth of the cloud computing market, the number and scale of cloud data centers are expanding rapidly. While cloud data centers provide a large amount of computing power, generating tremendous energy consumption has become a fundamental issue in the financial and environmental fields. Improving quality of service and reducing energy costs are fundamental challenges for next-generation cloud data centers. Task scheduling in cloud data centers grows increasingly complex due to the heterogeneity of computing resources, intricate dependencies of jobs and rising expenses resulting from high energy consumption. Efficiently utilizing computing resources is crucial, so it is necessary to develop optimal strategies for job scheduling. This paper proposes a reinforcement learning-based task scheduler (E2DSched) for online scheduling of randomly arriving directed acyclic graph jobs in cloud data centers. E2DSched divides the scheduling process into three layers: task selection layer, server selection layer and frequency control layer. It achieves joint optimization of energy consumption and quality of service through three-layer cooperation. Finally, we compare E2DSched with various other algorithms, and the results show that E2DSched can provide excellent service with less energy consumption. [ABSTRACT FROM AUTHOR]

The storage requirement for distributed tracing can be reduced significantly by sampling only the anomalous or interesting traces that occur rarely at runtime. In this paper, we introduce an unsupervised sampling pipeline for distributed tracing that ensures high sampling accuracy while reducing the storage requirement. The proposed method, SampleHST-X, extends our recent work SampleHST. It operates based on a budget which limits the percentage of traces to be sampled while adjusting the storage quota of normal and anomalous traces depending on the size of this budget. The sampling process relies on accurately defining clusters of normal and anomalous traces by leveraging the distribution of mass scores, which characterize the probability of observing different traces, obtained from a forest of Half Space Trees (HST). In our experiments, using traces from a cloud data center, SampleHST yields 2.3 × to 9.5 × better sampling performance. SampleHST-X further extends the SampleHST approach by incorporating a novel class of Half Space Trees, namely Approximate HST, that uses approximate counters to update the mass scores. These counters significantly reduces the space requirement for HST while the sampling performance remains similar. In addition to this extension, SampleHST-X includes a Family of Graph Spectral Distances (FGSD) based trace characterization component, which, in addition to point anomalies, enables it to sample traces with collective anomalies. For such traces, we observe that the SampleHST-X approach can yield 1.2 × to 19 × better sampling performance. [ABSTRACT FROM AUTHOR]

Elastic optical networks (EONs) offer tremendous benefits to deal with the exponential increase of the data center traffic. The granularity offered in spectrum allocation supports efficient management of available bandwidth and accommodates multiple traffic to be routed through common links. However, this brings the inherent challenges of routing and spectrum allocation (RSA) constraints. This becomes more complex for elastic optical data center networks (EODCNs), wherein multiple requests arrive at the same time, requiring identical or different bandwidths and each request may have the same or different destination and paths. Also, data requested by different users could be of varying importance levels. Under such a scenario, maintaining the quality of service (QoS) by minimizing the probability of traffic failure and bandwidth blocking is a major task for service providers. To address these problems, we propose an enhanced methodology using path prediction and link-state analysis for efficient allocation of frequency slots and reuse of bandwidth for data centers connected through EONs. Our proposed strategy intents to minimize the number of blocked requests due to non-availability of resources and reduce the failure probability. We introduce here the concept of connectivity degree and Kuhn-Munkres multi-objective optimization for spectrum allocation. We also evaluate the call request blocking probability varying the number of data centers and traffic load. The obtained results show that the proposed algorithm is highly effective in reducing the traffic failure and blocking probability for EODCNs. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for high-speed data transmission in telecommunications and data centers has driven the development of advanced on-chip integrated electro-optic modulators. Silicon modulators, constrained by the relatively weak carrier dispersion effect, face challenges in meeting the stringent requirements of next-generation photonic integrated circuits. Consequently, there has been a growing interest in Pockels effect-based electro-optic modulators, leveraging ferroelectric materials like LiNbO3, BaTiO3, PZT, and LaTiO3. Attributed to the large first-order electro-optic coefficient, researchers have delved into developing modulators with expansive bandwidth, low power consumption, compact size, and linear response. This paper reviews the working principles, fabrication techniques, integration schemes, and recent highlights in Pockels effect-based modulators. [ABSTRACT FROM AUTHOR]

Modern service systems, like cloud computing platforms or data center environments, commonly face a high degree of heterogeneity. This heterogeneity is not only caused by different server speeds but also, by binding task-server relations that must be taken into account when assigning incoming tasks. Unfortunately, there are hardly any theoretical performance guarantees as these systems do not fall within the typical supermarket modeling framework which heavily relies on strong symmetry and homogeneity assumptions. In "Heavy-traffic universality of redundancy systems with assignment constraints," Cardinaels, Borst, and van Leeuwaarden provide insight in the performance of these systems operating under redundancy scheduling policies. Surprisingly, when experiencing high demand, these systems exhibit state space collapse and can achieve a similar level of resource pooling and performance as a fully flexible system, even subject to quite strict task-server constraints. Service systems often face task-server assignment constraints because of skill-based routing or geographical conditions. Redundancy scheduling responds to this limited flexibility by replicating tasks to specific servers in agreement with these assignment constraints. We gain insight from product-form stationary distributions and weak local stability conditions to establish a state space collapse in heavy traffic. In this limiting regime, the parallel-server system with redundancy scheduling operates as a multiclass single-server system, achieving full resource pooling and exhibiting strong insensitivity to the underlying assignment constraints. In particular, the performance of a fully flexible (unconstrained) system can be matched even with rather strict assignment constraints. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2385. [ABSTRACT FROM AUTHOR]

Worldwide, the majority of countries are actively devising strategies to address the challenges associated with unregulated and unmanageable development, the decline in environmental quality and the depletion of valuable agricultural land. This has led to a growing emphasis on understanding land use and land cover. In order to determine a better land use policy, legislators and planners need to know the current distribution of agricultural and urban lands, as well as information about changes in their proportions. Our approach combines data centred on main four themes—geology, slope gradient, hydrographic network and land use—in order to exploit classifier complementarities in our targeted agricultural study area of Tamlouka Basin, Algeria. Landsat 8 OLI-TIRs multispectral imagery and Shuttle Radar Topography Mission (SRTM-1arc v3) were used experimentally for classification and Digital Elevation Model (DEM) analysis. The classification's accuracy is confirmed by comparing the results of the decision tree classification with the validation samples. Results of the combination of several maps of classifications from the different methods show that the Tamlouka alluvial plain, having an area of 19,300 ha and an average slope gradient of less than 2°, drains the elevated reliefs that surround it via hydrographic network. The plain occupies 37% of the total basin area, with over of 60% being used for crop cultivation, regardless of fallow land areas in agricultural rotation at that time. The slope has been identified as a crucial factor determining land use patterns in the study area. This result can be used in prospective watershed management. [ABSTRACT FROM AUTHOR]

Overview: Data Collaboratives have gained traction as interorganizational partnerships centered on data exchange. They enhance the collective capacity of responding to contemporary societal challenges using data, while also providing participating organizations with innovation capabilities and reputational benefits. Unfortunately, data collaboratives often fail to advance beyond the pilot stage and are therefore limited in their capacity to deliver systemic change. The governance setting adopted by a data collaborative affects how it acts over the short and long term. We present a governance design model to develop context-dependent data collaboratives. Practitioners can use the proposed model and list of key reflective questions to evaluate the critical aspects of designing a governance model for their data collaboratives. Practitioners can develop a detailed understanding of the governance factors that influence the successful creation of data collaboratives. The governance design process model offers practitioners a tangible roadmap and concrete activities to help them revamp an existing data collaborative or create a new one. The list of reflective questions can guide practitioners' initial design efforts and help them assess the robustness of their data collaboratives' current governance models. [ABSTRACT FROM AUTHOR]