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The operation of a four-channel multiplexer, utilizing multimode interference (MMI) wavelength division multiplexing (WDM) technology, can be designed through the cascading of MMI couplers or by employing angled MMI couplers. However, conventional designs often occupy a larger footprint, spanning a few millimeters, thereby escalating the energy power requirements for the photonic chip. In response to this challenge, we propose an innovative design for a four-channel silicon nitride (Si3N4) MMI coupler with a compact footprint. This design utilizes only a single MMI coupler unit, operating within the O-band spectrum. The resulting multiplexer device can efficiently transmit four channels with a wavelength spacing of 20 nm, covering the O-band spectrum from 1270 to 1330 nm, after a short light propagation of 22.8 µm. Notably, the multiplexer achieves a power efficiency of 70% from the total input energy derived from the four O-band signals. Power losses range from 1.24 to 1.67 dB, and the MMI coupler length and width exhibit a favorable tolerance range. Leveraging Si3N4 material and waveguide inputs and output tapers minimizes light reflection from the MMI coupler at the input channels. Consequently, this Si3N4-based MMI multiplexer proves suitable for deployment in O-band transceiver data centers employing WDM methodology. Its implementation offers the potential for higher data bitrates while maintaining an exemplary energy consumption profile for the chip footprint. [ABSTRACT FROM AUTHOR]

A workflow is sent from the internet of things (IoT) in the real world to a distributed edge micro data centre (EMDC), which is assigned to various resources for the necessary computation process. Since optimising energy consumption and other computing processes are crucial, a significant issue arises involving energy consumption and task execution time in an EMDC-integrated computing system. This study presented a task scheduling and allocation method with two phases in optimising the energy consumption and execution time of scientific workflow processing. The first phase assigned the workflow as a Directed Acyclic Graph (DAG) to an EMDC with the least latency. A selection algorithm then considered location routing problems (LRP), EMDC queue waiting time to initiate scheduling a DAG task and the EMDC capacity. The second phase demonstrated the allocation and schedule of DAG tasks to the resources of selected EMDCs. Subsequently, a critical path (CP) was proposed using the Multi-Objective Firefly Optimisation with Dynamic Voltage and Frequency Scaling (CP-MOFFO-DVFS) algorithm. This study demonstrated a 20 to 37% improvement in energy savings than the state-of-the-art method, thus substantially reducing execution time. [ABSTRACT FROM AUTHOR]

To solve the problem that noise seriously affects the online monitoring of parts signals of outdoor machinery, this paper proposes a signal reconstruction method integrating deep neural network and compression sensing, called ADMM-1DNet, and gives a detailed online vibration signal monitoring scheme. The basic approach of the ADMM-1DNet network is to map the update steps of the classical Alternating Direction Method of Multipliers (ADMM) into the deep network architecture with a fixed number of layers, and each phase corresponds to an iteration in the traditional ADMM. At the same time, what differs from other unfolded networks is that ADMM-1DNet learns a redundant analysis operator, which can reduce the impact of outdoor high noise on reconstruction error by improving the signal sparse level. The implementation scheme includes the field operation of mechanical equipment and the operation of the data center. The empirical network trained by the local data center conducts an online reconstruction of the received outdoor vibration signal data. Experiments are conducted on two open-source bearing datasets, which verify that the proposed method outperforms the baseline method in terms of reconstruction accuracy and feature preservation, and the proposed implementation scheme can be adapted to the needs of different types of vibration signal reconstruction tasks. [ABSTRACT FROM AUTHOR]

Cloud computing has become one of the key technologies used for big data processing and analytics. User management on cloud platforms is a growing challenge as the number of users and the complexity of systems increase. In light of the user-management system provided by major cloud service providers, which cannot manage multiple types of user systems, this article proposed scale-out automated expansion user management for authorization synchronization to improve the efficiency and scalability of user management on cloud platforms. Three modules for user-automated expansion were designed and implemented to synchronize the authentication information from the cloud platform resource user to the data-processing user. Additionally, an optimized dynamically weighted load-balancing algorithm in Nginx is presented in this article that adjusts the weight according to load information such as CPU and memory usage, and a better load balance can be achieved. The effectiveness of the proposed user-management system is substantiated by comparing it with two existing infrastructures, including multiple data centers and the Huawei cloud platform. The experimental results validate the finding that scale-out automated expansion user management across the Huawei cloud platform can effectively synchronize data accessing authority with cloud resource utilizing authority. Furthermore, the optimized weighted load-balancing algorithm is also valuable for massive concurrent user registration based on limited cloud resources. In the future, this scale-out user-management system could be applied to other cloud platforms and extended by database synchronization to satisfy the needs of data sharing among multiple types of users belonging to different cloud platforms. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) technology, as a subset of the Internet of Things (IoT) in the concept of Industry 4.0 and, in the future, 5.0, will face the challenge of streamlining the way huge amounts of data are processed by the modules that collect the data and those that analyse the data. Given the key features of these analytics, such as reducing the cost of building massive data centres and finding the most efficient way to process data flowing from hundreds of nodes simultaneously, intermediary devices are increasingly being used in this process. Fog and edge devices are hardware devices designed to pre-analyse terabytes of data in a stream and decide in realtime which data to send for final analysis, without having to send the data to a central processing unit in huge local data centres or to an expensive cloud. As the number of nodes sending data for analysis via collection and processing devices increases, so does the risk of data streams being intercepted. There is also an increased risk of attacks on this sensitive infrastructure. Maintaining the integrity of this infrastructure is important, and the ability to analyse all data is a resource that must be protected. The aim of this paper is to address the problem of autonomous threat detection and response at the interface of sensors, edge devices, cloud devices with historical data, and finally during the data collection process in data centres. Ultimately, we would like to present a machine learning algorithm with reinforcements adapted to detect threats and immediately isolate infected nests. [ABSTRACT FROM AUTHOR]

In the era of big data, data centers with high energy consumption, interconnectivity, and load flexibility have developed rapidly. However, due to data privacy issues, the traditional power-system operational reliability assessment (ORA) struggles to precisely consider the load flexibility of data centers, leading to inaccurate evaluation. To this end, this article proposes an ORA method considering the load flexibility of data centers via the energy consumption elastic space. By transforming the inner operation constraints of data centers into an equivalent elastic space, the ORA does not require any private data to complete the evaluation. Specifically, the energy consumption model of data centers is established to accurately describe the load flexibility. Then, based on multi-parametric programming techniques, the energy consumption elastic space of data centers is characterized by data centers' power demand constraints, which do not involve privacy data, and no privacy concerns exist. Finally, the ORA model and the evaluation method based on the energy consumption elastic space can be constructed. With a lot of data center operation constraints being replaced by power demand constraints, the proposed method can complete an evaluation faster without accuracy loss. Its effectiveness is validated through simulations using the IEEE RTS 24-bus system and a provincial 661-bus system. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of cloud computing, efficient resource allocation and workload prediction are paramount for optimizing infrastructure utilization, cost management, and overall service quality. As a result, an efficient resource management approach involves pooling available resources among many customers in a manner that considers their energy usage, SLAs, and forecasting accuracy all at once. Based on the performance analysis results, it is determined that integrating these techniques would result in the most efficient and adaptable cloud data centre. We proposed a Model based on Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) for handling SLAs in the cloud from both the perspective of consumers and service providers. Subsequently, we delve into the methodology of applying deep CNN and LSTM models to the problem of workload prediction in cloud environments. This methodology encompasses data preprocessing, model architecture, training parameters, and the choice of performance metrics. To anticipate CPU consumption from time series data and detect SLA violations, we suggested a DCNN-LSTM model. The accuracy prediction, energy usage, CPU use, and Service Level Agreement monitoring are all a part of this model. The proposed method is effective in helping cloud providers cut down on service violations and associated fines. Regarding the composite metric of Energy SLA Violation, which assesses the combined aspects of energy use and adherence to Service Level Agreements (SLAs), DCNN-LSTM surpasses ARIMA-LSTM, CNN, LSTM, and ARIMA 6.8%, 10.88%, 16.6%, and 22.4%, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing technology provides access on demand to virtualized resources, services, and applications via a distributed network. In cloud data centers effective energy utilization is a critical concern in today's technology-driven world. Cloud data centers (CDC) are massive facilities that host and manage an enormous amount of data and computing resources. This article addresses the growing significance and energy-intensive nature of data centers. Due to the rapid growth of cloud computing, it offers on-demand access to resources globally and leads to substantial power consumption and carbon impact on the environment. They consume substantial amounts of energy, and optimizing their energy utilization is essential for reducing operational costs, minimizing environmental impact, and ensuring sustainable growth. To combat this, efficient energy-saving approaches using machine learning methods have been researched. ML methods hold great potential for enhancing energy efficiency in CDCs by analysing data, detecting patterns, and optimizing resource usage. The focus areas include CPU usage prediction, overload finding, underload estimation, selection, migration, and relocation of VMs to attain resources and improve energy utilization. The paper compares energy-saving results achieved by different machine learning techniques in data centers to minimize energy usage and meet service level agreements (SLA). The machine learning approaches reduce energy consumption from 1.6% to 88.5% compared to the benchmark approach mentioned, considering various settings and parameters. [ABSTRACT FROM AUTHOR]

ω Centauri is considered the most massive globular cluster of the Milky Way and likely the former nuclear star cluster of a Galaxy accreted by the Milky Way. It is speculated to contain an intermediate-mass black hole (IMBH) from several dynamical models. However, uncertainties regarding the location of the cluster centre or the retention of stellar remnants limit the robustness of the IMBH detections reported so far. In this paper, we derive and study the stellar kinematics from the highest-resolution spectroscopic data yet, using the Multi Unit Spectroscopic Explorer (MUSE) in the narrow field mode and wide field mode. Our exceptional data near the centre reveal for the first time that stars within the inner 20 arcsec (∼0.5 pc) counter-rotate relative to the bulk rotation of the cluster. Using this data set, we measure the rotation and line-of-sight velocity dispersion profile out to 120 arcsec with different centres proposed in the literature. We find that the velocity dispersion profiles using different centres match well with those previously published. Based on the counter–rotation, we determine a kinematic centre and look for any signs of an IMBH using the high-velocity stars close to the centre. We do not find any significant outliers >60 km s−1 within the central 20 arcsec, consistent with no IMBH being present at the centre of ω Centauri. A detailed analysis of Jeans' modelling of the putative IMBH will be presented in the next paper of the series. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) and soil freeze–thaw (FT) are two relatively active surface parameters that are significant to the sustainable development of the water–land–air–plant–human nexus. Over time, regional or global SM and FT datasets with different spatial resolutions have been developed. In response to the requirements of multiscale product validation and multisource uncertainty tracking, a soil moisture and soil temperature (ST) monitoring network in the Qinghai Lake Basin (QLB-NET) was established in September 2019. The QLB-NET is characterized by densely distributed in situ sites (82 sites) measuring SM and ST at 5-, 10-, and 30-cm depths, with 60 sites in a large-scale network covering an area of 36 km × 40 km and 22 sites evenly distributed across two small-scale 1 km × 1 km networks. Quantitative analyses of the in situ measurements show that the QLB-NET can provide stable and reliable ground truth for SM and FT over coarse grid scales, e.g., 36 km × 36 km, 25 km × 25 km, and 0.25° × 0.25°. When statistics are correspondingly performed over 50 out of 54, 25 out of 29, and 25 out of 28 sites, the results are described as follows: 1) the standard deviation of the mean SM varies between 0.0127 and 0.0196 m3 m−3, with the corresponding difference between the upper and lower quartiles being less than 0.02 m3 m−3; 2) the ground freeze–thaw state can be correctly identified with high probabilities ranging from 85.3% to 100% on two freeze–thaw transitional dates. The QLB-NET observed datasets are distributed online and will be continuously updated through cooperation with the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn), facilitating product validation and uncertainty tracking, spatiotemporal analysis of SM change and FT transition, optimization of the SM and FT retrieving algorithms and scaling methods, and development of the mountainous microwave radiative transfer model. SIGNIFICANCE STATEMENT: This study aims to introduce the newly constructed QLB-NET in detail, including the site deployment strategy, the installation and maintenance, the sensor calibration, and the characteristics and quality of the in situ SM and ST measurements. Quantitative analyses of the in situ measurements show that the QLB-NET can provide stable and reliable ground truth for SM and FT over coarse grid scales, e.g., the SMAP 36 km × 36 km grid. The high-quality QLB-NET measurements will facilitate research of product validation and uncertainty tracking, spatiotemporal analysis of SM change and FT transition, optimization of the SM and FT retrieving algorithms and scaling methods, and development of the mountainous microwave radiative transfer model. [ABSTRACT FROM AUTHOR]

This document presents the results of a study on the regioselective deprotonation of brominated pyridylthiophene compounds. The researchers found that the position of deprotonation could be switched by adding DMPU as a Lewis base, allowing for the selective deprotonation of each proton on the thiophene moiety. The subsequent halogen dance reaction resulted in the synthesis of four iodinated isomers from a single starting material. This method has potential applications in the synthesis of various substituted pyridylthiophene derivatives. The study was conducted at NAIST and supported by the ARIM Program of MEXT, Japan. [Extracted from the article]

As information propels are changing day-to-day and creating at an incredible speed, the data society is becoming more complex, competitive, and trapped to inventive changes and information blasts. They ought to for E-information organisations to clients in expansion to expanding and getting to be exceptionally essential, the result of the web-based e-learning and teaching environment has affected each perspective of library and data organisations in educational libraries and given present-day openings and challenges to the library table for consideration interior the knowledge-based society tallying electronic and intelligently media conveying, and Web-based information organisations, around the world organising, web-based computerised resources, etc. Parents are tasked for selecting and organising resources coordinating advocates on the way to find and utilise these and securing information no matter the course of action or development. The data change, and in this way, the data that are available online have made unused challenges to those routinely capable ethics. The rising challenges of getting and giving electronic data resources require overseers to vary their portion from customary overseers to information analysts by learning and applying present-day aptitudes to know the advancing innovations to supervise and supply quality online information organisations to the data society. Thus, the vision of the longer-term educational library capability must be to form a World Course Organised around the World Library and Data Center to supply helpful web-based quality information advantage to the client in time interior the e-learning environment. The longer-term dreams require changes in scholarly libraries, designs, and challenges a few times as of late, the library capability interior the learning environment, and in this way, the diverse changing parts of the directions work out library capability have been talked around amid this study. [ABSTRACT FROM AUTHOR]

Task scheduling based on temperature perception is beneficial for avoiding hotspots and optimizing the internal temperature distribution of data centers. However, the accuracy of task scheduling largely depends on the accuracy of temperature prediction. There are many features that affect the accuracy of temperature prediction in data centers, and the variation periods of these features vary greatly. Traditional machine learning models are difficult to accurately fit them. Therefore, this article proposes a step-by-step temperature prediction algorithm based on Gated Recurrent Unit (GRU). This algorithm establishes prediction models for important parameters such as CPU utilization and air conditioning temperature that affect temperature prediction, and uses the outputs of these two models as inputs for the server temperature prediction model to better fit the changes of feature values. The model combines the principle of thermal locality and integrates the temperature of upper and lower servers for joint modeling. Experiments show that our prediction model can accurately predict the inlet temperature evolution of the server with dynamic workload. RSME reaches 0.278 and the average prediction temperature difference is 0.633, which is much higher than the traditional model. In addition, this article also propose a minimum temperature difference scheduling algorithm based on temperature prediction model, which can effectively reduce the number of servers running at high temperature and low temperature in the data center, make the temperature of the data center more balanced and achieve better energy-saving compared with other baseline algorithms. [ABSTRACT FROM AUTHOR]

The quantitative characterization of the thermal conditions in the Tibetan Plateau has long been a focal point of global research. Downward shortwave radiation, as a crucial component, plays an important role in numerous land surface processes while also serving as a significant indicator of the plateau's thermal state. In order to gain a more comprehensive understanding of the Earth's radiation budget in the Tibetan Plateau region, this study undertook an evaluation of six radiation products (ISCCP-FH, CERES-SYN, GLASS DSR, Himawari-8, MCD18A1, and ERA5). Two sets of ground measurements (downward shortwave radiation values from 10 CMA sites and 6 sites provided by the National Tibetan Plateau Data Center) in 2015 and 2016 were used as validation data to verify the accuracy of the remote sensing products. The results show that in the Tibetan Plateau region, CERESC products show the highest accuracy among the six data products with a bias (relative bias) of −7.57 W/m2 (3.46%), RMSE (relative RMSE) of 32.77 W/m2 (14.99%), and coefficient of determination of 0.80. Among all products, only the ERA5 products overestimated the value of downward shortwave radiation in the Tibetan Plateau region with a bias (relative bias) of 15.62 W/m2 (7.14%). By employing a spatial resolution upscaling approach, we assessed the influence of varying spatial resolutions on the validation accuracy, with the results indicating minimal impact. Through an analysis of the impact of cloud factors and aerosol factors on the validation accuracy, it is deduced that ERA5, Himawari-8, and MCD18A1 products are significantly influenced by cloud factors, whereas the CERES-SYN product is notably affected by aerosol factors. [ABSTRACT FROM AUTHOR]

The identification of new psychoactive substances (compounds that mimic the effects of outlawed substances) poses a significant challenge due to their rapid emergence and continuous modifications. This phenomenon results in these molecules escaping legal regulation, allowing them to circumvent legislation. The phenethylamine class has garnered attention because its molecules replicate the effects of LSD and are associated with numerous cases of intoxication. In this study, we focused on three phenethylamines—2C-H, 25H-NBOH, and 25I-NBOMe—with crystallographic structures available in the Cambridge Crystallographic Data Center (CCDC) database. We conducted a systematic conformational analysis and compared the structural information obtained. Subsequently, we compared the spectra derived from this analysis with experimental details from the ENFSI database. Structural comparisons were made based on the RMSDs between the lower energy conformations and experimental crystallographic structures. Additionally, structures obtained from direct optimization were compared. We then simulated the spectra based on the X-ray structures and compared them with those in the experimental database. Interpretation was carried out using heat maps and PCA in Pirouette software. Combining in silico methods with experimental approaches provides a more comprehensive understanding of the characterization process of new psychoactive substances (NPSs). [ABSTRACT FROM AUTHOR]

An attempt was made to assess the quality of the International Data Centre (IDC) Reviewed Event Bulletin (REB) by comparing it with the Reviewed bulletin of the International Seismological Centre (ISC). To accomplish the task of comparison, the ISC Reviewed Bulletin events as downloaded from the ISC Web page for the month of October 2020 were used. The corresponding REB events as listed in the ISC bulletin were considered. During this period, a total of 3431 and 2652 (excluding 39 Infrasound events) events were considered for ISC and IDC bulletins, respectively. The comparison was performed using the information given in the header lines of the ISC bulletin from which matched and unmatched events could be extracted. The results showed that a total of 2315 events could be matched between the two bulletins. The percentage of matched events with relative location difference (D) < 1° was about 95.3% while the percentage of events with D ≥ 5o was about 0.35%. There were two potential events with IDC magnitude (mb) ≥ 4.0 and D ≥ 5°. The percentage of matched events with intersecting error ellipses was about 75.5%. Some results obtained from earlier studies which were produced by using similar approaches were also used in this study. In general, the results obtained in this study show that there was a slight difference when compared to the earlier comparison results. [ABSTRACT FROM AUTHOR]

Remote sensing data, whose dimensions increase exponentially and turn into big data with the new technologies, cause significant difficulties in transferring, storing, and processing because of consisting of gigantic coarse-grained files. This article proposes a novel two-phase big data management system on the geo-distributed private cloud that takes advantage of network topology and resource utilization in a distributed manner. The system optimizes resource allocation to facilitate efficient and extensive data analysis for remote sensing applications by minimizing file fragmentation, resulting in faster analysis. In order to simulate the proposed system, different network topologies are created using virtual machines. Moreover, the proposed method named performance-aware assignment is compared with well-known methods such as random assignment, Hungarian algorithm, and Hadoop Distributed File System, also famous in the big data era. The experimental results indicate that performance-aware assignment outperforms random assignment, Hungarian algorithm, and Hadoop Distributed File System, achieving 36%, 26%, and 71% more stored data, respectively, within the same time while also exhibiting lower IOPS values. In addition, it optimizes resource usage in data centers, which is particularly important for preventing resource exhaustion. [ABSTRACT FROM AUTHOR]

In early 2022, there were four low‐temperature weather processes with rain and snow in Hunan Province, China. Two processes occurred on January 28–29 (referred to as the "0128" process) and February 6–7 (referred to as the "0206" process), and they have overlapping areas of heavy snowfall and high intensity of short‐term snowfall. Multi‐source observation data and the National Centers for Environmental Prediction (NCEP) reanalysis data are used to analyze the characteristics of circulation background and mesoscale. In addition, the causes of heavy snowfall processes under the influence of the southern branch trough are discussed based on the dual‐polarization radar products at Changsha station. The results show that two processes are characterized by the rapid phase transformation of rain and snow, concentrated snowfall periods, and heavy snowfall at night. The short‐term snowfall intensity of the "0206" process is greater than that of the "0128" process. The high‐latitude blocking high of the "0206" process is stronger than that of the "0128" process, and the water vapor transport of the southerly jet in low levels in the "0206" process is also stronger. The organized development of cold cloud clusters from the meso‐β scale to the meso‐α scale indicates that the snowfall intensifies, and the maximum blackbody temperature gradient corresponds well to the center of heavy snowfall. The propagation that is similar to the train effect is an important reason for the heavy snowfall process. The vertical variation of the ZH and the bright band of dual‐polarization parameters can determine the phase transformation between rain and snow. When the ZH and ZDR bright bands are 1–3 km away from the ground, the phase state is rain if the ZH near the ground is greater than 0 dBZ and the CC is close to 1; the phase state is the rain‐snow mixed phase if the CC is less than 0.95. When the bottom of the ZH bright band decreases, the CC/ZDR bright band disappears, the near‐surface CC is greater than 0.99 and the ZDR is less than 1 dB, the rain turns to snow. Compared with the "0128" process, the characteristics of the bright ring during the rainfall period of the "0206" process are more obvious, the precipitation intensity judged from the larger ZH and KDP is larger, and the phase transformation is faster due to more significant cooling effect caused by precipitation. [ABSTRACT FROM AUTHOR]

The article investigates what happens in the platform economy from the point of view of the algorithm-mediated relations established between corporations, governments, and users. From an assemblage approach, the article argues that a new order is emerging whose most salient feature is the collaborative alliances between governments and users versus the large corporations behind the platforms. A direct consequence of this is increasing regulation of platforms' economic activities. Based on the survey of four legal cases, the article applies a mapping method to identify the capacities exercised by the components of this algorithmic assemblage and observe that, while big tech corporations continue to occupy a central position, users and governments are pushing for the assemblage's reterritorialisation and, hence, their Deperipheralisation. Considering this novel trend, the article concludes that the most politically relevant pillar of the new identity of the algorithmic assemblage is a new social contract centred on data rights, literacy, and sovereignty. [ABSTRACT FROM AUTHOR]

Recently, low-cost, low-power sensors have made wireless communication possible for Wireless Sensor Networks (WSNs), which are growing in popularity and bringing about tremendous improvement. Nevertheless, their autonomy eventually wanes because WSN nodes run entirely on batteries. The network lifetime is affected by this energy constraint. Therefore, the proposed method uses Quantum-based Optical sensors with the CUCKOO-ANN method for Enhanced Energy-Efficient Wireless Communication to overcome the existing issues. Initially, the Quantum-based optical sensors helped collect the data from the location and were also used for energy-efficient communication. It transfers the data very quickly and securely without any interruption. Next, the CUCKOO-ANN method optimises the real-time data and provides efficiency. Therefore, it helps to find an optimal route between the user and the Base station. The concept that has been presented exhibits excellent potential as it can efficiently fulfil customer service level agreements, lower average execution times, and improve data centre energy efficiency. Experimental results demonstrate that the proposed energy-efficient method minimises energy consumption, optimises data transmission, improves the network lifetime, and enhances the scalability of the network. [ABSTRACT FROM AUTHOR]

Herein, the time series storage method of multi-valued attribute data, aimed at improving the efficiency of writing and querying data in "energy" big data centers is reported. Through rule construction and rule iteration of feature sequence, the time series features of multi-valued attribute data are extracted, and the "component attribute nearest neighbor propagation method" is used for clustering; the data are divided into cold, warm, and hot data. A storage engine with a solid state disk layer, mechanical hard disk layer, and memory layer has been designed, and the efficiency of data writing and query through migration operation is improved. The experimental results demonstrate that this method can effectively extract the time series features of multi-valued attribute data, and the Pre value of the clustering time series is higher than 0.93, which effectively improves the data writing and query efficiency of the energy big data center. [ABSTRACT FROM AUTHOR]

As the internet data centers are mushrooming, the energy consumption and carbon emission of data centers are increasing rapidly. To cut down the electricity cost and carbon emission of the data centers, we proposed an optimization method to reduce the electricity cost and carbon emission in geo-distributed multiple data centers. In the proposed method, the carbon tax is considered in the overall operation cost to optimize the carbon emission. The spatial and temporal flexibility of computational workload is fully utilized by considering the difference in renewable energy power output, local electricity and carbon emission of multiple geo-distributed data centers to achieve a better performance. Furthermore, the nonlinear characteristics of the power loss of uninterruptible power supply (UPS) are considered in the optimization. To verify the proposed optimization method, simulation of six cases is carried out with realistic data, and results have proved the proposed method can reduce the operational costs by 4.93%-12.7% and decrease carbon emissions by up to 10%. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the role of research data preservation for enhanced data usage among agricultural researchers in Tanzania. Specifically, the study aimed to examine the data preservation methods used by agriculture researchers, find out how long agriculture researchers preserve their agriculture research data, and determine factors that influence agriculture researchers on their choice of data preservation methods for use. The study employed a cross-sectional research design. The study employed both qualitative and quantitative approaches. A survey was conducted to collect data in 11 research institutions. A simple random sampling technique was used to select 204 respondents from the study area while purposive sampling techniques were used to select 11 agriculture research institutions including 10 Tanzanian Agricultural Research Institution (TARI) centers, and Sokoine University of Agriculture (SUA). Also, 12 respondents were selected purposively for an in-depth interview as key informants. The study adopted Data Curation Centre (DCC) Lifecycle Model to explain data preservation process. Findings indicated that a majority of more than 90% of researchers preferred to preserve their data using different storage devices such as field notebooks, computers, and institutional libraries. Moreover, findings indicated that about 74% of agricultural researchers preferred to preserve their data for more than 6 years after the end of the project. Findings also indicated factors that influence researchers in the choice of data preservation methods to be easy to reach, cost-effective storage devices, support to use the devices, adequate infrastructure for data preservation, and reliable power supply. It can be concluded that there is yet a great role of research data preservation in enhancing data usage among researchers in Tanzania. It is recommended that the government should establish an agricultural research data bank to guarantee permanent availability of data at all times when needed. [ABSTRACT FROM AUTHOR]

The structure of Ni(3-amino-4,4′-bipyridine)[Ni(CN)4] (or known as Ni-BpyNH2) in powder form was determined using synchrotron X-ray diffraction and refined using the Rietveld refinement technique (R = 8.8%). The orthorhombic (C mca) cell parameters were determined to be a = 14.7218(3) Å, b = 22.6615(3) Å, c = 12.3833(3) Å, V = 4131.29(9) Å3, and Z = 8. Ni-BpyNH2 forms a 3-D network, with a 2-D Ni(CN)4 net connecting to each other via the BpyNH2 ligands. There are two independent Ni sites on the net. The 2-D nets are connected to each other via the bonding of the pyridine "N" atom to Ni2. The Ni2 site is of six-fold coordination to N with relatively long Ni2–N distances (average of 2.118 Å) as compared to the four-fold coordinated Ni1–C distances (average of 1.850 Å). The Ni(CN)4 net is arranged in a wave-like fashion. The functional group, –NH2, is disordered and was found to be in the m -position relative to the N atom of the pyridine ring. Instead of having a unique position, N has ¼ site occupancy in each of the four m -positions. The powder reference diffraction pattern for Ni-BpyNH2 was prepared and submitted to the Powder Diffraction File (PDF) at the International Centre of Diffraction Data (ICDD). [ABSTRACT FROM AUTHOR]

Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. Although the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, such as to increase performance and to make a running system more robust. We instantiate and empirically examine the preceding idea for software-defined networking—a key enabling technology for modern data centers and Internet of Things applications. Using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open source synthetic and industrial data, indicates that compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, it reduces the frequency of adaptation interventions over time. In addition, we show that for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss. [ABSTRACT FROM AUTHOR]

Superapps (ie apps that integrate the features of multiple applications for a more convenient user experience) have become pervasive among Internet users. This case study examines a recent disruption to one such application: KakaoTalk — the most widely used messaging application in South Korea. Specifically, the case study examines a fire incident at the SK C&C data centre, which caused an extended outage for one of South Korea's leading tech companies — Kakao Corp. The review of this event reveals how ineffective disaster readiness resulted in inadequate fire response, leading to serious ripple effects across the data centre. During the outage, cyber-security threats rose. As a result of these disruptions, Kakao users turned to competitor apps, resulting in changing market dynamics. This case study highlights the unforeseen costs and socio-economic influences caused by such interruptions, highlighting the importance of holistic risk management strategies. [ABSTRACT FROM AUTHOR]

Martin Cooper MBCS slips on his winter coat and visits the Stellium data centre in Newcastle-upon-Tyne, a place famous for football, bridges and – it transpires – highly desirable levels of rain and cold. [ABSTRACT FROM AUTHOR]

Purpose: In rotating machines, bearings are essential components, but these have a limited life and are subject to degradation. This paper discusses detecting anomalies in the bearing's various components, such as the inner ring, outer ring, and ball, by processing the bearing's vibration signal. Methodology: Self-Organizing Maps (SOMs) and Variational Mode Decomposition (VMD) are used to automate diagnosing bearing defects. The approach's performance was evaluated using the Bearing Data Center (BDC) database, which includes data collection, preprocessing, and SOM algorithm implementation. Results and Conclusions: The proposed method accurately classifies bearing fault types and can aid in early detection and proactive maintenance to prevent catastrophic equipment failure. The findings also show that VMD decomposition and envelope demodulation effectively detect various bearing faults. Therefore, using advanced machine learning techniques to accurately diagnose rotating machine failures is necessary. [ABSTRACT FROM AUTHOR]

In today's world, cloud computing is widely used in various applications and services, which refers to the utilization of computational resources as per user requirements through the Internet. However, despite the numerous advancements in cloud services and applications, there are various security threats associated with it, mainly due to data outsourcing to third-party controlled data centers. In this context, this paper introduces a new model called Attribute-based Advanced Security Model (AASM) for Reliable Data Sharing in the Cloud. This model combines the Advanced Encryption Technique (AET) with Attribute-Based Signature (ABS) to ensure secure data sharing in the Cloud while efficiently controlling data access. The model enables encrypted access control from the data owner's side with advanced access privileges, ensuring user privacy with an anonymous authentication model using ABS. By implementing these measures, the model provides security for cloud providers and users while safeguarding against malicious attacks. The effectiveness of the proposed model is evaluated based on factors such as time complexities, security and accountability. [ABSTRACT FROM AUTHOR]

This paper proposes to utilise an innovative transformation KDE (Kernel Density Estimation) method in order to more accurately calculate the sea state parameter distribution tails and to extrapolate well. This transformation KDE method is applied in predicting the probability distribution tails of a measured ocean wave dataset at National Data Buoy Center Station 51101, and its accuracy has been verified through comparisons with the prediction results via the parametric method. Next, the transformation KDE method is utilised for deriving an accurate 50-year environmental contour line based on the aforementioned measured wave dataset. The derived environmental contour line and some other contour lines obtained using parametric contour approaches are then applied for predicting the 50-year design PTO (Power-Take-Off) force values for a point absorber Wave Energy Converter (WEC). It is concluded that the predicted 50-year design PTO force value based on the proposed transformation KDE contour is more accurate. [ABSTRACT FROM AUTHOR]

This paper is an extended version of a previously reported conference paper regarding a low-power design for NAND Flash. As the number of bits per NAND Flash die increases with cost scaling, the IO data path speed increases to minimize the page access time with a scaled CMOS in IOs. The power supply for IO buffers, namely, VDDQ, decreases from 3 V to 1.2 V, accordingly. In this paper, the way in which a reduction in VDDQ can contribute to power reduction in the BL path is discussed and validated. Conventionally, a BL voltage of about 0.5 V has been supplied from a supply voltage source (VDD) of 3 V. The BL path power can be reduced by a factor of VDDQ to VDD when the BL voltage is supplied by VDDQ. To maintain a sense margin at the sense amplifiers, the supply source for BLs is switched from VDDQ to VDD before sensing. As a result, power reduction and an equivalent sense margin can be realized at the same time. The overhead of implementing this operation is an increase in the BL access time of about 2% for switching the power supply from VDDQ to VDD and an increase in the die size of about 0.01% for adding the switching circuit, both of which are not significant in comparison to the significant power reduction in the BL path power of the NAND die of about 60%. The BL path is then designed in 180 nm CMOS to validate the design. When the cost for powering the SSD becomes quite significant, especially for data centers, an additional lower voltage supply, such as 0.8 V, dedicated to BL charging for read and program verifying operations may be the best option for future applications. [ABSTRACT FROM AUTHOR]

Optical interconnects are being discussed as a replacement for conventional electrical interconnects and are expected to be applied for future generations of high-performance supercomputers and data centers. Benes networks have attracted much attention because they require only 2 × 2 optical switches, which reduce the cost of rearrangeable nonblocking. However, optical power imbalances can significantly challenge receiver sensitivity. In this work, insertion loss (IL) fairness has been proposed and applied to the field of switches to achieve a relative balance of optical path data transmission in Benes networks. Fairness can be achieved when the port count is small (4 × 4) if the IL between ports is balanced. When the number of ports is moderate (8 × 8), we must use a suitable algorithm or determine the appropriate operating wavelength to minimize the power imbalance. An efficient two-step algorithm (ETS) has particular advantages in solving the path fairness problem and mitigating the power imbalance. As the number of ports increases, the switch states and topology jointly deteriorate the power imbalance. Finally, the ETS algorithm narrows the dynamic range requirement to 13.66 dB, with a 2 dB improvement. It achieves an extinction ratio of 24 dB and a bandwidth of 375 GHz, which outperforms the conventional 32 × 32 Benes network, respectively. [ABSTRACT FROM AUTHOR]

This article examines the environmental impact of digital infrastructures, specifically focusing on cloud computing. It analyzes J.R. Carpenter's web-based poem-essay, "The Gathering Cloud," which emphasizes the physical reality of data storage and transmission. The article raises ethical questions about cloud computing and calls for a better understanding of our dependence on digital technologies. It also discusses the excessive electricity consumption of data centers and the lack of transparency in the industry. Carpenter's work incorporates images, texts, and historical artifacts to explore the intersections of art, technology, and science, while also encouraging readers to consider the ethics and environmental impact of their media consumption choices. [Extracted from the article]