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ABSTRACT In this study, a polar seismic acquisition station based on the FPGA + ARM architecture was successfully developed. This system can achieve real‐time multifunctional acquisition and transmission of seismic signals and environmental information in the extreme low‐temperature environment of Antarctica at −40°C. The system adopts a modular design, with high‐precision data acquisition encoded by the main controller and uploaded to the host computer in real‐time. Integrated display controls enable real‐time data display and acquisition station location mapping for user interaction. We propose improving data transmission in traditional seismic acquisition stations by adopting a distributed computing model with data segment return. Integrating the first arrival picking algorithm as an IP core will reduce computational demands on the data processing center. This acquisition system features high precision (24‐bit), a wide dynamic range (> 130dB), low noise (0.5 μ$$ \upmu $$V rms@40dB), and high‐precision timing control (200 ns). It supports real‐time wireless data transmission over 120 channels up to 1 km. The system also implements a self‐storage plus wired and wireless networking solution, allowing flexible networking based on actual needs. Test results indicate that the ice sheet thickness at the acquisition site is approximately 526.49 m, validating the experimental design's effectiveness and providing technical support for subsequent drilling and seismic exploration. [ABSTRACT FROM AUTHOR]

"Cloud computing" hosts software and other services in remote data centers that customers can access worldwide. The user may access all the services and applications online. The IT industry has benefited greatly from the proliferation of cloud computing. On the flip side, organizations moved their operations to the cloud as a result of industrial automation. A surge in demand for cloud computing was directly correlated to the quick migration of businesses. Businesses looking to minimize expenses without sacrificing service quality will find this approach to be ideal. Considering the meteoric rise of cloud computing, service providers are delighted. Contrarily, distributing resources is a challenging task. Cloud computing overcomes some of its most fundamental obstacles, one of which is the load-balancing approach employed by load-balancers to economically optimize costs while minimizing time expenditures. Quick services for cloud customers and minimal cost for cloud providers are the goals of the optimal resource allocation method. This research suggests a novel approach to increase task processing time, which can aid in increasing cloud computing's load balancing capabilities. The proposed method Enhanced Throttled Load Balancing Algorithm (ETLBA) is an upgrade to the original Throttled Algorithm, which efficiently performs resource allocation and load balancing. The proposed ETLBA is contrasted with the existing algorithms, Round Robin, Active Monitoring Load Balancing Algorithm (AMLBA) and Throttled Load Balancing Algorithm (TLBA) to display the efficacy. Cloud Analyst tool simulates the proposed and existing methods. According on the results of the simulations, the proposed algorithm ETLBA achieves better outcomes than the popular existing algorithms in terms of processing time, request processing time, and datacenter cost. It shows 18% reduction in response time, 7% reduction in data center processing time, 16% reduction in data center request processing time and 4% less data center cost compared to the existing solutions. ETLBA performs better by selecting virtual machines using a prioritized index table and consumption index. It limits idling resources, improves response as well as reduces processing times, and cloud costs compared to conventional solutions. [ABSTRACT FROM AUTHOR]

Cyber‐physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero‐day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search‐cascade regional convolutional neural network (hybrid TS‐Cascade R‐CNN) to detect cyberattacks. The proposed model combines the hybrid transit‐search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards. [ABSTRACT FROM AUTHOR]

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In the near future, commercially accessible quantum computers are anticipated to revolutionize the world as we know it. These advanced machines are predicted to render traditional cryptographic security measures, deeply ingrained in contemporary communication, obsolete. While symmetric cryptography methods like AES can withstand quantum assaults if key sizes are doubled compared to current standards, asymmetric cryptographic techniques, such as RSA, are vulnerable to compromise. Consequently, there is a pressing need to transition towards post-quantum cryptography (PQC) principles in order to safeguard our privacy effectively. A challenge is to include PQC into existing protocols and thus into the existing communication structure. In this work, we report on the first experimental IPsec tunnel secured by the PQC algorithms Falcon, Dilithium, and Kyber. We deploy our IPsec tunnel in two scenarios. The first scenario represents a high-performance data center environment where many machines are interconnected via high-speed networks. We achieve an IPsec tunnel with an AES-256 GCM encrypted east–west throughput of 100 Gbit/s line rate. The second scenario shows an IPsec tunnel between a wireless NVIDIA Jetson and the cloud that achieves a 0.486 Gbit/s AES-256 GCM encrypted north–south throughput. This case represents a mobile device that communicates securely with applications running in the cloud. [ABSTRACT FROM AUTHOR]

Rapid processing with low-latency and high-throughput is a critical requirement for the applications of big data streams. However, the interferences among stream processing tasks in a data center decrease the utilization of the computational resources and prolong the latency of the tasks. Thus, we study an optimal scheduling method for processing a big data stream on heterogeneous servers with multicores in a data center. We model the big data stream processing and the scheduling problem with four objects or factors which are streaming data items, processing tasks, computational nodes and the cores inside each computational node. An interference model based on regression analysis and a prediction model based on the Autoregressive Integrated Moving Average are presented. Then, we propose a two-stage scheduling method including the fine-grained core scheduling and the coarse-grained node scheduling. In the core scheduling stage, we design a core scheduling algorithm named CS_TDF. In the node scheduling stage, we design a node scheduling algorithm named NS_ITF for a single time window and a continuous scheduling algorithm named PS_UIM for the entire data stream in all time windows. The experimental results show that our scheduling method achieves low interference and high computational resource utilization. [ABSTRACT FROM AUTHOR]

This article discusses the potential use of fractional exhaled nitric oxide (FENO50) as a biomarker for chronic obstructive pulmonary disease (COPD). The study found that FENO50 levels remained stable throughout the day in stable COPD patients, regardless of the use of inhaled corticosteroids. This suggests that FENO50 measurements can be taken at any time of the day without affecting the results. The study also compared the within-day variability of FENO50 with that of blood eosinophil count (B-EOS) and found that FENO50 had lower variability. These findings support the use of FENO50 as a reliable biomarker in COPD research and clinical practice. [Extracted from the article]

Water quality monitoring systems that are enabled by the Internet of Things (IoT) and used in water applications to collect and transmit water data to data processing centers are often resource-constrained in terms of power, bandwidth, and computation resources. These limitations typically impact their performance in practice and often result in forwarding their data to remote stations where the collected water data are processed to predict the status of water quality, because of their limited computation resources. This often negates the goal of effectively monitoring the changes in water quality in a real-time manner. Consequently, this study proposes a new resource allocation method to optimize the available power and time resources as well as dynamically allocate hybrid access points (HAPs) to water quality sensors to improve the energy efficiency and data throughput of the system. The proposed system is also integrated with edge computing to enable data processing at the water site to guarantee real-time monitoring of any changes in water quality and ensure timely access to clean water by the public. The proposed method is compared with a related method to validate the system performance. The proposed system outperforms the existing system and performs well in different simulation experiments. The proposed method improved the baseline method by approximately 12.65% and 16.49% for two different configurations, demonstrating its effectiveness in improving the energy efficiency of a water quality monitoring system. [ABSTRACT FROM AUTHOR]

In analyzing terrestrial water storage (TWS) data observed by Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On satellites, quantifying uncertainties proves challenging due to the scarcity of sufficient independent observations of mass changes at scales commensurate with these missions. Moreover, owing to the diverse geophysical background models and processing techniques utilized by data processing centers, reaching consistent mass change estimations within specific regions of published solutions often proves arduous. We, therefore, quantified the uncertainty of the GRACE‐derived TWS changes by using the generalized three‐cornered hat method without relying on any prior knowledge and fused it to generate a higher‐quality solution. The findings reveal that of the six solutions, the Center for Space Research spherical harmonics (SH) solution exhibits the lowest uncertainty and highest signal‐to‐noise ratio (SNR) at both global and basin scales, and the Goddard Space Flight Center Mascon solution outperforms other Mascon counterparts. The fusion solution has an average 36.56% reduction in uncertainty and a 1.92‐fold improvement in SNR at the basin scale, and the improvement in SNR is particularly significant in regions with drastic mass changes. The global distribution patterns of the uncertainties associated with Mascon and SH solutions exhibit distinct differences. Mascon solutions result in significant signal leakage around regions characterized by the most substantial global mass variability. Additionally, transient mass changes triggered by super earthquake events in the ocean also produce similar "scars" in the global spatial distribution of uncertainties. The analysis of 142 basins worldwide shows that basins with more significant TWS annual oscillations have larger uncertainties but also better SNR. Plain Language Summary: Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow‐On have furnished direct estimates of total water storage from the surface to the groundwater, providing invaluable benefits for studying the continental water cycle and quantifying water resources on a global scale. However, uncertainties in GRACE observations are difficult to assess, and there is some inconsistency in the mass variation obtained in some regions for solutions published by different institutions. In this work, we quantified the uncertainty in over 19 years of GRACE and GRACE Follow‐On (GRACE‐FO) solutions published by several major GRACE data processing centers and found significant differences in the global distribution patterns of uncertainty for different types of solutions. A more highly accurate fusion solution was subsequently generated based on this. The fused solution resulted in improved effectiveness of GRACE data, especially in regions with drastic mass changes. A reference and benchmark for the selection and use of GRACE products in future studies can be provided by this fusion solution. Key Points: Global spatial discrepancies in uncertainty and signal‐to‐noise ratio (SNR) are markedly pronounced between Gravity Recovery and Climate Experiment (GRACE) spherical harmonics and MasconsBasins exhibiting larger annual oscillations in terrestrial water storage have larger uncertainties but also superior SNRsThe fusion GRACE solution exhibits outstanding performance, enhancing the SNR by 1.92× across 142 basins globally [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Mobile Networks & Applications" introducing the topic of vision-based mobile information systems (MIS). The editorial highlights the importance of researching and developing robust transmission, optimal allocation, and safe services for mobile information systems. It also mentions that the special issue of the journal focuses on mobile vision-based theory, analysis, and applications. The editorial provides a brief overview of seven articles in the first section, which cover topics such as image matching, recognition, watermarking, feature diagnosis, motion, and trajectory detection. The second section includes six articles that focus on allocation, clustering, mining, and other research and applications in MIS. The editorial acknowledges the support of reviewers and funding sources. [Extracted from the article]

In order to relieve the pressure of medical resources and meet the demand of human health real-time detection, a remote health detection algorithm based on sensor signal purification was designed. In the wearable health device, health parameter sensors such as integrated temperature sensors were set up, and the results of human health sensing signals were collected by wireless communication technology and transmitted to a remote data processing center. The data processing center used the wavelet threshold method to denoise the human health sensing signal, and selected the joint time–frequency decomposition method to purify the human health sensing signal. The purified result of the sensor signal was set as the input sample of the hidden Markov model. The Baum-Welch local optimization algorithm was used to update the parameters of the hidden Markov model until the posterior probability of the model approached the maximum value, and the human health detection results were output. The experimental results show that the acquisition error of human physiological parameters is less than 0.5% when the sensor signal is purified by this algorithm, and the abnormal health state of testers such as falling, high body temperature and high blood pressure is accurately detected. [ABSTRACT FROM AUTHOR]

Today, increasing communication of Internet of Things (IoT) devices, wearable sensors, healthcare applications and cloud providers provides power and energy consumption for high processing personal data and healthcare records. Cloud-edge data centers are processing enormous amount of electrical power resulting in high performance computing. One of the important challenges in this problem is Virtual Machine (VM) service placement that attempts to enhance energy management of VMs to cloud-edge service providers dynamically to support Service Level Agreement (SLA) metrics in IoT systems. Service placement is a very important issue because if VMs are not confident in the SLA of their critical data, IoT applications will not want to use existing resources safety. Providing a way to increase the Quality of Service (QoS) factors and energy efficiency of the service placement in the IoT environment is an important and critical issue because the use of IoT devices and wearable sensors increases energy consumption in human life in smart contracts. Therefore, this paper presents a hybrid Genetic algorithm and social spider optimization (GA-SSO) algorithm for an energy-aware service placement model in the IoT to manage data congestion and system safety. After conducting studies and comparisons, the accuracy and superiority of the proposed model were established. Experimental results show that the energy consumption with the proposed GA-SSO algorithm can be reduced 24% and we can achieve to the performance of QoS factors with fitness function 88% with compare to the other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

The Protein Data Bank (PDB) is the single global archive of atomic‐level, three‐dimensional structures of biological macromolecules experimentally determined by macromolecular crystallography, nuclear magnetic resonance spectroscopy or three‐dimensional cryo‐electron microscopy. The PDB is growing continuously, with a recent rapid increase in new structure depositions from Asia. In 2022, the Worldwide Protein Data Bank (wwPDB; https://www.wwpdb.org/) partners welcomed Protein Data Bank China (PDBc; https://www.pdbc.org.cn) to the organization as an Associate Member. PDBc is based in the National Facility for Protein Science in Shanghai which is associated with the Shanghai Advanced Research Institute of Chinese Academy of Sciences, the Shanghai Institute for Advanced Immunochemical Studies and the iHuman Institute of ShanghaiTech University. This letter describes the history of the wwPDB, recently established mechanisms for adding new wwPDB data centers and the processes developed to bring PDBc into the partnership. [ABSTRACT FROM AUTHOR]

Cloud computing and cloud storage have contributed to a big shift in data processing and its use. Availability and accessibility of resources with the reduction of substantial work is one of the main reasons for the cloud revolution. With this cloud computing revolution, outsourcing applications are in great demand. The client uses the service by uploading their data to the cloud and finally gets the result by processing it. It benefits users greatly, but it also exposes sensitive data to third-party service providers. In the healthcare industry, patient health records are digital records of a patient's medical history kept by hospitals or health care providers. Patient health records are stored in data centers for storage and processing. Before doing computations on data, traditional encryption techniques decrypt the data in their original form. As a result, sensitive medical information is lost. Homomorphic encryption can protect sensitive information by allowing data to be processed in an encrypted form such that only encrypted data is accessible to service providers. In this paper, an attempt is made to present a systematic review of homomorphic cryptosystems with its categorization and evolution over time. In addition, this paper also includes a review of homomorphic cryptosystem contributions in healthcare. [ABSTRACT FROM AUTHOR]

With the evolution of geographically distributed data centers in the Cloud Computing landscape along with the amount of data being processed in these data centers, which is growing at an exponential rate, processing massive data applications become an important topic. Since a given task may require many datasets for its execution and the datasets are spread over several different data centers, finding an efficient way to manage the datasets storage across nodes of a Cloud system is a difficult problem. In fact, the execution time of a task might be influenced by the cost of data transfers, which mainly depends on two criterias. The first one is the initial placement of the input datasets during the build‐time phase, while the second is the replication of the datasets during the runtime phase. The replication is explicitly considered when datasets are being migrated over the data centers in order to make them locally available wherever needed. Data placement and data replication are important challenges in Cloud Computing. Nevertheless, many studies focus on data placement or data replication exclusively. In this paper, a combination of a data placement strategy followed by a dynamic data replication management strategy is proposed, with the purpose of reducing the associated cost of all data transfers between the (distant) data centers. Our proposed data placement approach considers the main characteristics of a data center such as storage capacity and read/write speeds to efficiently store the datasets, while our dynamic data replication management approach considers three parameters: the number of replicas in the system, the dependency between datasets and tasks and the storage capacity of data centers. The decision of when and whether to keep or to delete replicas is determined by the fulfillment of those three parameters. Our approach estimates the total execution time of the tasks as well as the monetary cost, considering the data transfers activity. Our experiments are conducted using Cloudsim simulator. The obtained results show that our proposed strategies produce an efficient data management by reducing the overheads of the data transfers, compared to both a data placement without replication (by 76%) and the selected data replication approach from Kouidri et al. (by 52%), and by improving the financial cost. [ABSTRACT FROM AUTHOR]

Data centers are being distributed worldwide by cloud service providers (CSPs) to save energy costs through efficient workload allocation strategies. Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload processing. Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers (DCs). However, operating cost savings during workload processing, which also considers stringmatching techniques in geo-distributed DCs, remains unexplored. In this research, we propose a novel string matching-based geographical load balancing (SMGLB) technique to mitigate the operating cost of the geo-distributed DC. The primary goal of this study is to use a string-matching algorithm (i.e., Boyer Moore) to compare the contents of incoming workloads to those of documents that have already been processed in a data center. A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy. On the contrary, if no match can be discovered, the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices, the number of active servers, on-site green energy, and traces of incoming workload. The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques. [ABSTRACT FROM AUTHOR]

Considering that the operation of power transmission and transformation equipment is not timely discovered due to the untimely data integration, a multi-dimensional heterogeneous data clustering algorithm for power transmission and transformation equipment based on multimodal deep learning is proposed. The multi-modal deep learning method is used to mine relevant data and measure the similarity between the data, which can improve the accuracy of subsequent multi-bit heterogeneous data clustering of power transmission and transformation equipment. Set up a clustering center and process data clustering to complete multi-dimensional heterogeneous data clustering of power transmission and transformation equipment. The experimental results show that the method has high clustering accuracy in the clustering of voltage deviation overrun times, voltage harmonic total distortion rate overrun times, and voltage flicker overrun times. [ABSTRACT FROM AUTHOR]

The safe operation of bridges is a major issue related to the national economy and people's livelihood. With the rapid development of the water transport industry, the problems of ship yaw and bridge collision avoidance occur frequently, which cause serious harm to the bridge structure and safety. However, the current bridge health monitoring system is difficult to feed back the monitored information in time, and in the process of collecting information by wireless sensors, information errors or missing problems easily occur, which hinders the normal monitoring of bridge health. With the rapid development of the Internet and the gradual maturity of wireless sensor technology, how to achieve complete and effective collection and feedback of monitoring information has become a hot research topic and an urgent problem. Therefore, on the basis of Internet technology, this study perceives, collects and processes the ship information in the bridge monitoring area covered by the network through the wireless sensor network, uses the embedded wavelet neural network model to denoise the monitoring data, and finally transmits it to the data processing center, thus establishing a centralized remote real-time Long-span bridge health monitoring system based on the Internet. The experimental results show that the monitoring deformation displacement curve based on the Internet centralized remote real-time long-span bridge health monitoring system technology presents a stable fluctuation state. The cumulative shape variable fluctuates in the range of - 5 mm to 5 mm, indicating that the deformation trend of the bridge has always existed. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) devices are gaining popularity due to the available high-speed internet services provided by the 4G networks. When the 5G is fully deployed, the IoT devices will be increased to trillions in operation and may cause heavy load on the Cloud data centers for data processing, which in turn can cause latency and network congestion for immediate result response. Cloud computing environment is not suitable for many Internet of things (IoT) user applications, hence, fog computing concept is used, as its distributed approach addresses the needs of IoT and industrial IoT applications, as well as the immense amount of live data generation, which would be expensive and tedious to ship off the cloud for handling and investigation. Haze registering arrangement decreases the organization transmission capacity required and lessens the to and fro correspondence among sensors and the cloud server farm, which can improve IoT execution. Mist computing can be considered as mini-data centers which are subset of Cloud data centers. This paper presents the possibility of resource optimization, service migration of mobile IoT devices from location one to next location and balancing of load in fog when the new service is trying to connect. [ABSTRACT FROM AUTHOR]

The work examines the heat exchange characteristics of a condenser in which heat removal of the refrigerant condensation occurs due to natural convection and radiation cooling. The heat exchanger is designed to reduce energy costs for the removal of condensation heat. Unlike traditional air cooling condensers, it uses radiation cooling, a method of heat removal based on its transmission in the form of infrared radiation through the planet’s atmosphere into the surrounding outer space. A method for calculating the thickness of the radiating plate has been developed. To minimize material consumption and cost, the distance between the tubes is reduced to 40 mm, and the thickness of the aluminum radiating plate is reduced to 0.32 mm. The inner diameter of the coolant channels is 1 mm. For the experimental study of the condenser, an experimental stand working on R134a refrigerant was developed. Theoretical and experimental studies of heat transfer have been carried out. The heat transfer coefficient of the heat exchanger, reduced to the area of the radiating surface, was from 10.3 ± 1.36 to 18.7 ± 2.47 W·m–2·°C–1, when the condensation temperature was 12.8...21.9 °С higher than the temperature of atmospheric air. The operability of the condenser is shown both during the day and at night, in the presence of precipitation in the form of rain and snow, and a high level of cloudiness. The material capacity and filling of the refrigerant in the condenser are comparable to the characteristics of air-cooled condensers with forced air circulation. At the same time, it does not consume electricity. It can be used in stationary refrigeration systems (in data processing centers, commercial refrigeration equipment, air conditioners) to increase their energy efficiency. [ABSTRACT FROM AUTHOR]

The number of data centers worldwide is increasing year by year, mostly because of the development of cloud services and applications. In the near future, the rate of construction of data centers will grow, with a corresponding increase in their electrical energy consumption. The requirements of the reliability of the electrical power supply of data centers are one of the highest among industrial power consumers, since uninterrupted power supply is critically important for the continuous functioning of server hardware. The assessment of electrical power supply reliability is one of the most important parts of the design process of data centers. However, the speed of the development of new power equipment does not always make it possible to use classical probabilistic and statistical methods for reliability assessment. Therefore, the development of new methods for reliability assessment based on alternative approaches, which can eliminate the disadvantages of probabilistic and statistical methods, are of great interest. This paper discusses the alternative method for analyzing the reliability of electrical power supply for the case of data centers. The method defines the reliability through the internal information of the system that characterizes the system's topology, flows of information, energy, and matter in the system. [ABSTRACT FROM AUTHOR]

Data centers are located centralized to do computation and accessing huge amount of data by the network devices which are interconnected to form the network path. Servers are stacked, data storage is placed in them. Data server backup and server redundancies are the recovery mechanisms implemented. Data centers compute, store, distribute the data by processing them and the data center controls all the interconnected network equipment in the distributed network. In current, RAID system is implemented to avoid the service disruptions due to disk failures, the availability of system and services are achieved with this expensive model. But still the availability is lost, and service disruptions happen due to disk failures, the machine learnings models to be used to predict the disk failures well in advance. Data center has increased usage of system with increased data storage, the failure in disc makes the system failed and down time increases. Analysis on the methods of problems in disk and methods of disk availability and predict the disk failure is the main goal. Various machine learning models are identified and discussed along with the SMART parameters for measuring the failure of the disk. Improved method of Ensembling of trees, random forest and boosting techniques are also discussed. [ABSTRACT FROM AUTHOR]

Multifocal intraocular lenses incorporate a variety of design considerations, including dimensioning of the base monofocal shape and the diffraction grating. While studying three different lens models, we present a practical approach for mathematical modelling and evaluation of these geometries. Contrary to typical lens measurement methods, non-contact measurements were performed on the Alcon SN6AD1, HumanOptics MS 612 DAY and the AMO ZMA00 lenses using a confocal microscope. Subsequent data processing includes centering, tilting correction, filtering and an algorithmic decomposition into a conic and polynomial part and the diffraction grating. Lastly, evaluation of fitting parameters and grating shape is done to allow for inferences about further optical properties. Results and analysis show the confocal microscope to be a suitable imaging method for lens measurements. The processing of this data enables the reconstruction of the annular diffraction grating over the complete lens diameter. Apodization, near addition and diffraction efficiency characteristics are found utilizing the grating shape. Additionally, near-optical axis curvature, asphericity and higher order polynomials are identified qualitatively from the reconstruction of the monofocal base form. Derived properties also include the lens optical base and addition power. By making use of the surface geometries, as well as the lens' material and thickness, a full lens model can be created for further studies. In summary, our analytical approach enables the insight to various intraocular lens design decisions. Furthermore, this procedure is suitable for lens model creation for research and simulation. [ABSTRACT FROM AUTHOR]

This paper is concerned with the first work on the integration of digital twin (DT), 5G, cloud platform, and virtual reality (VR) technologies for unmanned aerial vehicles (UAVs) autonomy development. DT focuses on connecting the virtual and physical world as an emerging strategic technology. Initially, it was implemented through mirror models of physical objects to realize the monitoring of their whole life cycle in the manufacturing area. In recent years, DT technologies have been applied in different fields, and some typical DT solutions have been proposed to solve complex system problems. In this paper, we study the problem of how to combine the DT and other emerging technologies for UAV autonomy development and supervision, aiming to propose a basic DT framework to integrate DT and UAVs as reference rules for building DT systems, which includes four parts, that is, Virtual Space, Real Space, Service Center, and Data and Model Processing Center. Based on the proposed basic DT framework, a cloud-based DT system is then further constructed in which cloud platform, 5G, and VR are integrated seamlessly. The running and implementation processes of each subsystem are introduced in detail. Multiple experiments are conducted to verify the usefulness of proposed DT system, that is, real-time system monitoring and cloud processing, VR connection, human–robot interaction through VR technology, and so on. The experimental results show that the proposed DTUAV system can be used in the interaction of virtual and physical systems, remote supervision, intelligence integration of swarm of unmanned vehicles, and so on. The development in our work introduces the DT into unmanned system applications and can promote relevant research in this direction. All implementation codes of the system will be shared in https://github.com/DTUAV. [ABSTRACT FROM AUTHOR]

The operation of coal mines is intricately linked with emitting a large quantity of coal mine methane, and in most cases, this methane releases into the atmosphere. In total, according to statistics, coal mining enterprises emit 8% of anthropogenic methane, determining a contribution to greenhouse gas emissions to the amount of 17%. There are various means for coal mine methane utilization. In this study, the concept "Coal-Energy-Information" is proposed. This concept implies both the construction of data processing centers on the industrial sites of coal mines and the usage of coal mine methane. Coal mine methane can be used as a primary energy source for the energy supply of data processing center consumers as well as coal mine consumers with necessary energy resources (electricity, heat, and cooling). Within the framework of the proposed concept, three options of coal mine methane utilization are considered. The first option is the use of gas genset for electrical and thermal energy generation (cogeneration) and their usage for coal mine and constructed data processing centers and consumers' power supply. The second option is absorption refrigerator usage (with coal mine methane direct burning) for cooling the IT equipment of constructed data processing centers. The last one is the use of a gas genset and absorption refrigerator (trigeneration) for constructed data processing centers' and coal mine consumers' energy supplies (electricity, heat, and cooling). In conclusion, it is noted that proposed concept is closely correlated with the program for the development of the coal industry in Russia for the period up to 2035, since it allows creating a base for the implementation of innovative technologies based on digital platforms that ensure the development of coal mining technology without the constant presence of personnel in underground mining facilities. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) generates countless amounts of data, much of which is processed in cloud data centers. When data is transferred to the cloud over longer distances, there is a long latency in IoT services. Therefore, in order to increase the speed of service provision, resources should be placed close to the user (i.e., at the edge of the network). To address this challenge, a new paradigm called Fog Computing was introduced and added as a layer in the IoT architecture. Fog computing is a decentralized computing infrastructure in which provides storage and computing in the vicinity of IoT devices instead of sending to the cloud. Hence, fog computing can provide less latency and better Quality of Service (QoS) for real-time applications than cloud computing. In general, the theoretical foundations of fog computing have already been presented, but the problem of IoT services placement to fog nodes is still challenging and has attracted much attention from researchers. In this paper, a conceptual computing framework based on fog-cloud control middleware is proposed to optimally IoT services placement. Here, this problem is formulated as an automated planning model for managing service requests due to some limitations that take into account the heterogeneity of applications and resources. To solve the problem of IoT services placement, an automated evolutionary approach based on Particle Swarm Optimization (PSO) has been proposed with the aim of making maximize the utilization of fog resources and improving QoS. Experimental studies on a synthetic environment have been evaluated based on various metrics including services performed, waiting time, failed services, services cost, services remaining, and runtime. The results of the comparisons showed that the proposed framework based on PSO performs better than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As a powerful feature extraction tool, a convolutional neural network (CNN) has strong adaptability for big data applications such as bearing fault diagnosis, whereas the classification performance is limited when the quality of raw signals is poor. In this paper, stochastic resonance (SR), which provides an advanced feature enhancement approach for weak signals with strong background noise, is introduced as a data pre-processing method for the CNN to improve its classification performance. First, a multiparameter adjusting bistable Duffing system that can achieve SR under large-parameter weak signals is introduced. A hybrid optimization algorithm (HOA) combining the genetic algorithm (GA) and the simulated annealing (SA) is proposed to adaptively obtain the optimized parameters and output signal-to-noise ratio (SNR) of the Duffing system. Therefore, the data optimization based on the multiparameter-adjusting SR of Duffing system can be realized. An SR-based mapping method is further proposed to convert the outputs of the Duffing system into grey images, which can be further processed by a normal CNN with batch normalization (BN) layers and dropout layers. After verifying the feasibility of the HOA in multiparameter optimization of the Duffing system, the bearing fault data set from the CWRU bearing data center was processed by the proposed fault enhancement classification and identification method. The research showed that the weak features of the bearing signals could be enhanced significantly through the adaptive multiparameter optimization of SR, and classification accuracies for 10 categories of bearing signals could achieve 100% and those for 20 categories could achieve more than 96.9%, which is better than other methods. The influences of the population number on the classification accuracies and calculation time were further studied, and the feature map and network visualization are presented. It was demonstrated that the proposed method can realize high-performance fault enhancement classification and identification. [ABSTRACT FROM AUTHOR]

Real-time tracking welding with the assistance of structured light vision enhances the intelligence of robotic welding, which significantly shortens teaching time and guarantees accuracy for user-customized product welding. However, the robustness of most image processing algorithms is deficient during welding practice, and the security regime for tracking welding is not considered in most trajectory recognition and control algorithms. For these two problems, an adaptive feature extraction algorithm was proposed, which can accurately extract the seam center from the continuous, discontinuous or fluctuating laser stripes identified and located by the CNN model, while the prior model can quickly remove a large amount of noise and interference except the stripes, greatly improving the extraction accuracy and processing speed of the algorithm. Additionally, the embedded Pauta criterion was used to segmentally process the center point data stream and to cyclically eliminate outliers and further ensure the accuracy of the welding reference point. Experimental results showed that under the guarantee of the above-mentioned seam center point extraction and correction algorithms, the tracking average error was 0.1 mm, and even if abnormal trajectory points existed, they did not cause welding torch shaking, system interruption or other accidents. [ABSTRACT FROM AUTHOR]

Das Management digitaler Forschungsdaten ist ein zentraler Aspekt im Wissenschaftsbetrieb. Bereits lange unterstützen Datenzentren und Servicestellen Forschende bei Fragen zum Forschungsdatenmanagement (FDM). Dabei sollten sich die Organisation und inhaltliche Ausgestaltung dieser Servicestrukturen an den täglichen FDM-Bedarfen der Forschenden orientieren. Mit diesem Beitrag soll ein entsprechender Ansatz für ein bedarfsorientiertes FDM in den Geisteswissenschaften vorgestellt und diskutiert werden. Im Fokus stehen dabei die Beratungs- und Protokollierungspraxis des Data Center for the Humanities (DCH) an der Universität zu Köln sowie konkrete Methoden zur Identifizierung und Modellierung von FDM-Bedarfskategorien und -workflows zur formalen Beschreibung von geisteswissenschaftlichem FDM. Research Data Management (RDM) is a core aspect of scientific work. Data centers and service points have long supported researchers in questions about RDM. This paper will illustrate and discuss an approach for organizing RDM in the humanities, focusing in particular on the daily requirements of researchers as far as RDM is concerned. It will present two main topics: on the one hand, the RDM-counseling and documentation processes of the Data Center for the Humanities (DCH) at the University of Cologne; on the other, concrete methods for identifying and modeling RDM-requirement categories and -workflows. [ABSTRACT FROM AUTHOR]

Summary: In today's world, cloud computing is an emerging service, and it has proved to be a profit‐oriented business model. A drastic growth was observed in cloud computing during the last decade because of its easy access to services. As the number of users are increasing dynamically, load balancing is required to handle the user's load. Load balancing algorithms minimize the data center processing time and increase the throughput. Therefore cloud service providers need the best dynamic strategies. In this work, the proposed Efficient Throttled load balancing algorithm improves the performance in the data center environment. Our experimental results indicate that the Efficient Throttled load balancing algorithm improves the average overall response time by 6.47% and average data center processing time by 20.74%, compared to the Throttled load balancing algorithm. [ABSTRACT FROM AUTHOR]

The Indigenous Autonomous University of Mexico, located in Sinaloa, receives indigenous students from all the states of the republic, it has a student dormitory and a dining room free for them. At UAIM students have access to library services and computer centers, in addition to wireless internet, so they do not have connectivity problems. However, when confinement began and they retired to their homes, the complete lack of connectivity for some was evident, because there is no internet service nearby or poor service. The objective of this article is to identify the main difficulties faced by students according to the teachers to maintain quality education in the virtual classrooms and propose possible strategies to replace content for the return. face-to-face. This essay was done through the biographical narrative methodology with a report on the experiences of 8 professors of the Law degree at the Autonomous Indigenous University of Mexico who for two years worked in the Classroom at Home with their students, their strategies and frustrations to complete course after course. Monthly reports issued by the Coordination of the school were consulted. It is concluded that a great effort was made by all the actors involved for the preparation of the students, however, the distance, the times and the deficiencies, but above all the lack of formal preparation of the teachers in the use of the educational platforms, but above all due to the extreme flexibility during the pandemic, the quality decreased a lot and did not allow more than 80% of the expected learning to be achieved among students and that as teachers they must be aware of it, but above all do it know the students. [ABSTRACT FROM AUTHOR]

The article presents the discussion on Risk 4.0 refering to managing risk around digitization. Topics include emphasizing risk intelligence and proactive risk management for helping a planned reaction to high-level foreseen risks; and digitalization refering to the quality improvement of business processes and decision making using digitization.

• Long-range forward Brillouin scattering temperature sensor is proposed and performed. • The approach for quickly acquiring an optimal R 0, m mode spectrum is presented. • Frequency shift of remote FBS sensing shows a linear correlation with temperature. • Impact of SMF length in the main sensing unit on the FBS gain spectrum is discussed. • Impact of SMF length in transmission channel on the FBS gain spectrum is discussed. We propose and experimentally implement a long-range forward Brillouin scattering (FBS) temperature sensor, in which a 25-m highly nonlinear fiber (HNLF) is included in a Sagnac ring and is employed as the main sensing unit, and a 40-km standard single-mode fiber (SMF) is used as the transmission channel to connect the main sensing unit to the data processing center. The approach for quickly acquiring an optimal R 0, m mode spectrum is presented. After taking into account the signal-to-noise ratio, sensitivity, and Lorentzian spectral shape, the R 0, 17 mode was selected to perform temperature sensing. The frequency shift of the long-range and local FBS sensing demonstrates a linear correlation with the temperature change from 30 ℃ to 60 ℃, with respective frequency shift-temperature coefficients of 73.1 kHz/℃ and 73.4 kHz/℃. Impact of the length of the SMF as the main sensing unit and the SMF as the transmission channel on the FBS gain spectrum has also been discussed, respectively. The proposed long-range FBS scheme is expected to find applications in the investigation of submarine oil and gas pipelines, as well as temperature gradients in underwater and mountainous environments. [ABSTRACT FROM AUTHOR]

The supply temperature in district heating systems has traditionally been controlled using feedforward – a robust and well-validated approach for district heating networks with few producers and relatively high supply temperatures. The transition towards lower temperature district heating networks allows for efficient reuse of excess heat from, e.g., industrial processes and data centers. Excess heat is often intermittent, cannot always be assumed to be possible to control with a centralized controller, and can cause temperature disturbances in the grid. Closing the loop using PID control is challenging due to the process's time-varying nature and long time delays. Model Predictive Control (MPC) suffers from a higher complexity, long computational times, and the need for a well-validated and maintained centralized model. The paper suggests a semi-decentralized approach using the Smith predictor with an event-driven assignment of active controllers and sensors. A reduced order model based on a more comprehensive state space model is derived and used for gain scheduling and input–output pairing using the normalized relative gain array. The focus is on temperature disturbance rejection, and appropriate tuning rules and controller structures are suggested. Simulation results show that the proposed control structure can handle various types of temperature disturbances, even in the presence of model estimation errors. • A semi-decentralized approach for disturbance rejection in district heating grids. • Tuning rules for a filtered Smith predictor with bounded time-delay. • Reduced order model for prediction and parameter scheduling for disturbance rejection. [ABSTRACT FROM AUTHOR]

With the intensification of the pace of modern urban life and the increase of work pressure, people have higher requirements for the safety and comfort of home. However, the rapid development of Internet of Things technology, wireless communication technology, embedded technology, and data fusion technology has made this desire possible, thus making intelligent and safe. Using wireless sensor network, intelligent home system can realize the real-time collection of object location and state information, remote operation of home equipment, as well as the monitoring of the information of personnel activities, etc. However, how to efficiently analyze and integrate these information to be applied to the intelligent home system is one of the problems that need to be solved; further study of the smart home system of personal behavior identifies fire monitoring and remote monitoring of mining applications. Such information collection and integration of technology and method are given based on wireless sensor network transfer of key technologies; intelligent home security monitoring system based on Bayesian networks is proposed and a card. According to the theoretical fire monitoring method, and based on the Bayesian belief network personal behavior identification model, this paper provides the theory and method for the remote safety monitoring and control in the intelligent home environment. In this paper, based on the multisensor data fusion of smart home security, real-time monitoring alarm platform is built. Through the multisensor detection module for the home fire gas human infrared monitoring, the detected data is transmitted to the data fusion center for processing; processed results are uploaded to the cloud through the Internet. When abnormal conditions occur, users can log in to the mobile phone client to view abnormal alarm results. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rise of big data has brought severe challenges to data storage, which also means that computing resources and storage resources have become centralized. In the past, business data could end up after being processed locally or at the secondary node. Now, all data must be uploaded to each data center for unified processing. Data collection also requires users to access data center services and then send them back, which undoubtedly increases the pressure on the backbone network. This also means that the annual private line cost of the enterprise will increase with the growth of network traffic. Large enterprises often use multiple lines between network nodes to improve link utilization and network reliability. However, in most cases, the line utilization of the backup line is usually low, because the traditional routing algorithm forwards according to the optimal path. Load sharing or traffic scheduling is achieved by adjusting interface cost values ​​or BGP parameters. In large networks, it often affects the entire body. Each adjustment requires detailed planning and validation. At the same time, due to the diversity of applications, different applications have different business characteristics. If the network can distinguish different applications, sense network conditions, and adjust the QoS and traffic paths of applications as needed, the link utilization of leased lines and the network quality of applications will be significantly improved. SDN separates the control plane from the data plane and intelligently controls the network forwarding policy through the global controller, which improves the flexibility and security of the network. Based on the traditional large-scale enterprise WAN architecture, this paper proposes a complete traffic scheduling solution based on SDN to achieve secure, controllable, and flexible scheduling of enterprise WAN network resources. [ABSTRACT FROM AUTHOR]

Copyright of Diyala Journal for Pure Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is about the Data Processing Center (DPC) working within the RadioAstron project. Since the launch of the space radio telescope to orbit in July 2011, the DPC underwent significant quantitative and qualitative changes as the project developed. This paper describes the schemes, structure, stages of the processing center development, current upgrades and new technologies introduced in the DPC throughout the entire period of its operation. For technical reasons, the scheduled observations in the project were terminated in January 2019. However, still the work of the DPC continued. Today, the most valuable thing we have is information. There is now a unique archive of service and scientific data of the project in our possession, and a data bank of the RadioAstron project is underway, which will ensure a proper access to scientific and service information for all interested experts, as well as provide an opportunity for an additional data analysis. [ABSTRACT FROM AUTHOR]

Nowadays, GPU clusters are available in almost every data processing center. Their GPUs are typically shared by different applications that might have different processing needs and/or different levels of priority. In this scenario, concurrent kernel execution can leverage the use of devices by co-executing kernels having a different or complementary resource utilization profile. A paramount issue in concurrent kernel execution on GPU is to obtain a suitable distribution of streaming multiproccessor (SM) resources among co-executing kernels to fulfill different scheduling aims. In this work, we present a software scheduler, named FlexSched, that employs a run-time mechanism with low overhead to perform intra-SM cooperative thread arrays (a.k.a. thread block) allocation of co-executing kernels. It also implements a productive online profiling mechanism that allows dynamically changing kernels resource assignation attending to the instant performance achieved for co-running kernels. An important characteristic of our approach is that off-line kernel analysis to establish the best resource assignment of co-located kernels is not required. Thus, it can run in any system where new applications must be immediately scheduled. Using a set of nine applications (13 kernels), we show our approach improves the co-execution performance of recent slicing methods. Moreover, our approach obtains a co-execution speedup of 1.40 × while slicing method just achieves 1.29 × . In addition, we test FlexSched in a real scheduling scenario where new applications are launched as soon as GPU resources become available. In this scenario, FlexSched reduces the average overall execution time by a factor of 1.25 × with respect to the time obtained when proprietary hardware (HyperQ) is employed. Finally, FlexSched is also used to implement scheduling policies that guarantee maximum turnaround time for latency sensitive applications while achieving high resource use through kernel co-execution. [ABSTRACT FROM AUTHOR]