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Essity Hygiene and Health Aktiebolag has been issued a patent for a method of installing hygiene equipment in places like hospitals, airports, and shopping malls. The patent addresses the need for efficient management and configuration of the equipment, particularly in large facilities. The invention aims to automate the setup and configuration process, making it easier and more accurate for installation workers. The patent describes a method for a mobile computing device to install a sensor device at a location of a piece of hygiene equipment, obtaining information about the identification and location of the sensor device, and associating this information together. The patent also includes additional claims related to obtaining information about the type of hygiene equipment and consumables, storing the associated information, transmitting it to a central server device, and performing tests to ensure correct installation. The patent was filed by Gunilla Himmelmann on December 21, 2018, and published online on January 9, 2024. [Extracted from the article]

The white papers aims to provide support and insights into the factors associated with the technology options currently available to the sector for lowering the carbon footprint of data centre operations. The purpose of this series of white papers is to help inform decision making as data centre owners play their part in reducing and removing greenhouse gas emissions in order to reach carbon neutrality by 2030.". [Extracted from the article]

This article deals with efforts of the U.S. Office of Personnel Management (OPM) to digitize the records of government employees. OPM officials are in charged of the new effort to create an online human resources recordkeeping and analysis system that charts the careers of about 1.8 million employees. It will eventually replace the paper filed in folders. Offering managers and their staff real-time access to such information, agency officials say, will streamline operations and improve workforce planning. The high profile e-government initiative will eliminate personnel paperwork throughout executive agencies. Officials anticipate a potential savings of about $235 million in ten years. OPM officials have reached several milestones in the Enterprise Human Resources Integration (EHRI) project, including the creation of a large data repository in which all employee records will reside. The data warehouse will hold information on education, personnel actions such as transfers and promotions, retirement benefits and other activities. It will include training and payroll information, which officials did not previously collect. The repository contains nine years of federal workforce history. OPM officials plan to add six more years soon, said Rhonda Diaz, EHRI project manager.

The article reports that Stora Enso will sell its Summa mill in Hamina, Finland to Google Inc. in 2009. It mentions that the site is expected to be used by the latter as a new data centre. It states that the sale, which is valued at €40 million and covers all plants, is expected to be completed by the end of this year's first quarter. It claims that the latter company has already 40 data centres around the world to support its computing activities.

The article focuses on a white paper titled "The Optimum Data Center: How Modular Data Centers Transcend Containers" released by NxGen Modular in January 2012 which explores the evolution of containerized data centers into more customizable, scalable and maintainable modular data centers. The key differences between containerized and modular data centers addressed in the paper are cited. NxGen will also conduct a survey on the key considerations in choosing a modular data center solution.

The article discusses the construction of data centers in Sweden and Finland due to cheaper costs. It discusses the purchase and renovation of a former paper mill in Finland for storing user data of American multinational corporation Google, and the construction of a data center for online social networking service Facebook in Luleå, Sweden. An overview of Sweden's hydroelectric system and hydroelectric infrastructure is presented.

The article discusses the challenges of integrating unstructured data, such as handwritten lab notebooks, into modern data management systems. It highlights the potential value of this data for further research and the development of more efficient research environments. The article also explores advancements in machine learning, such as object detection technology and optical character recognition, that can effectively organize and process information from unstructured data sources. The author emphasizes the importance of incorporating this valuable knowledge into research workflows and envisions a future where AI-based systems play a key role in scientific discovery. [Extracted from the article]

The author discusses advancements in the use of field-programmable gate arrays (FPGAs) in datacenters.

This paper presents a fast array of wimpy nodes — FAWN — an approach for achieving low-power data-intensive datacenter computing. FAWN couples low-power processors to small amounts of local flash storage, balancing computation and I/O capabilities. FAWN optimizes for per node energy efficiency to enable efficient, massively parallel access to data. The key contributions of this paper are the principles of the FAWN approach and the design and implementation of FAWN-KV — a consistent, replicated, highly available, and high-performance key-value storage system built on a FAWN prototype. Our design centers around purely log-structured datastores that provide the basis for high performance on flash storage, as well as for replication and consistency obtained using chain replication on a consistent hashing ring. Our evaluation demonstrates that FAWN clusters can handle roughly 350 key-value queries per Joule of energy — two orders of magnitude more than a disk-based system. [ABSTRACT FROM AUTHOR]

Flash is a widely used storage device in portable mobile devices such as smart phones, digital cameras, and MP3 players. It provides high density and low power, properties that are appealing for other computing domains. In this paper, we examine its use in the server domain. Wear-out has the potential to limit the use of Flash in this domain. To seriously consider Flash in the server domain, architectural support must exist to address this lack of reliability. This paper first provides a survey of current and potential Flash usage models in a data center. We then advocate using Flash as an extended system memory usage model—OS managed disk cache—and describe the necessary architectural changes. Specifically we propose two key changes. The first improves performance and reliability by splitting Flash-based disk caches into separate read and write regions. The second improves reliability by employing a programmable Flash memory controller. It changes the error code strength (number of correctable bits) and the number of bits that a memory cell can store (cell density) in response to the demands of the application. [ABSTRACT FROM AUTHOR]

Then, this paper expounds the construction of public health informatization based on the grass-roots management system of community health records, and discusses the construction of a health information platform centered on the health records of community residents. Keywords: Community Health; Health and Medicine; Information Technology; Information and Data Archiving; Public Health EN Community Health Health and Medicine Information Technology Information and Data Archiving Public Health 3044 3044 1 05/02/23 20230505 NES 230505 2023 MAY 5 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- A new study on information and data archiving is now available. [Extracted from the article]

The Millinocket, Maine rural area is designated as a Qualified Opportunity Zone with tax benefits, with the 13 acre site set to house a 60MW Nautilus data centre. Nautilus is well-known for its data centre barge in California, which uses river water to cool the facility's servers, and it will now replicate that technology in Maine, US at a new facility located at an old disused paper mill. [Extracted from the article]

Generation of massive data in the digital age leads to possible violations of individual privacy. The search for personal data becomes an increasingly recurrent exposure today. The present work corresponds to the area of differential privacy, which guarantees data confidentiality and robustness against invasive identification attacks. This area stands out in the literature for its rigorous mathematical basis capable of quantifying the loss of privacy. A differentially private method based on regression models was developed to prevent inversion attacks while retaining model efficacy. In this paper, we propose a novel approach to improve the data privacy based on regression models under heteroscedasticity, a common aspect, but not studied, in practical situations of differential privacy. The influence of privacy restriction on the statistical performance of the estimators of model parameters is evaluated using Monte Carlo simulations, including a study of performance associated with test rejection rates for the proposed approach. The results of the numerical evaluation show high inferential distortion for stricter privacy restrictions. Empirical illustrations with real-world data are presented to show potential applications. [ABSTRACT FROM AUTHOR]

• A distribution measurement indicator is proposed to measure the distribution of individuals in a population. • A distribution enhancement module is introduced to increase the distribution of the individuals. • A limit optimization variation strategy of the best individual is taken to generate local solutions. • The threshold for judging the distribution level of individuals is adjusted adaptively. In general, for the iteration process of an evolutionary algorithm (EA), there exists the problem of uneven distribution of individuals in the target space for both multi-objective and single-objective optimization problems. This uneven distribution significantly degrades the population diversity and convergence speed. This paper proposes an adaptive hybrid evolutionary immune algorithm based on a uniform distribution selection mechanism (AUDHEIA) for solving MOPs efficiently. In AUDHEIA, the individuals in the population are mapped to a hyperplane, which is correlated with the objective space and are clustered to increase the diversity of solutions. To improve the distribution of the solutions, the mapped hyperplane is evenly sectioned. With the constantly changing distribution during the iteration, a threshold as a standard for judging the distribution level is adjusted adaptively. When the threshold is not satisfied in the corresponding interval, the distribution enhancement module is activated. Then, the same number of individuals should be selected in each interval. However, sometimes, there are insufficient or no individuals in the interval during the iterative process. To obtain sufficient individuals, the limit optimization variation strategy of the best individual is adopted. Experiments show that this algorithm can escape from local optima and has a high convergence speed. Moreover, the distribution and convergence of this algorithm are superior to the peer algorithms tested in this paper. [ABSTRACT FROM AUTHOR]

This paper considers the time-dependent project scheduling problem (TPSP). We propose a time wave neural network (TWNN) framework that is able to achieve the global optimal solution (viz., the optimal project schedule) of the TPSP, which is very difficult to obtain using conventional methods (e.g., Dijkstra’s algorithm). The proposed TWNN is a time wave neuron-based neural network without a requirement for any training. In the design of a TWNN, the overall project network of the TPSP is viewed as a neural network, while each node is considered as a wave-based neuron. With this new perspective, the wave-based neuron is constructed based on seven parts: an input, a wave receiver, a neuron state, a time-window selector, a wave generator, a wave sender, and an output. The first three parts are used to receive the waves coming from the predecessor neurons, the fourth part is used to choose the optimal feasible time window, and the remaining three parts are utilized to generate waves for the successive neurons. The main idea of a TWNN is based on the following mechanism: a wave generated from a neuron (node) means that all previous arcs (subprojects) of this neuron have been completed. In particular, the global optimal project scheduling is obtained when a wave is generated by the final destination neuron. To evaluate the performance of a TWNN, the well-known project scheduling problem library data sets are modified and considered in a comparative analysis. Numerical examples are also utilized to demonstrate the robustness of the method. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel computing model, called probe machine (PM). Unlike the turing machine (TM), PM is a fully parallel computing model in the sense that it can simultaneously process multiple pairs of data, rather than sequentially process every pair of linearly adjacent data. We establish the mathematical model of PM as a nine-tuple consisting of data library, probe library, data controller, probe controller, probe operation, computing platform, detector, true solution storage, and residue collector. We analyze the computation capability of the PM model, and in particular, we show that TM is a special case of PM. We revisit two NP-complete problems, i.e., the graph coloring and Hamilton cycle problems, and devise two algorithms on basis of the established PM model, which can enumerate all solutions to each of these problems by only one probe operation. Furthermore, we show that PM can be implemented by leveraging the nano-DNA probe technologies. The computational power of an electronic computer based on TM is known far more than that of the human brain. A question naturally arises: will a future computer based on PM outperform the human brain in more ways beyond the computational power? [ABSTRACT FROM AUTHOR]

Since the past 10 years, data storage or enterprise/private server locations have been shifted from individual hardware memory to cloud computing server. Hyper-scale data center, which provides users/subscribers with cloud computing services, is increasing in many parts of the world. Consequently, both of the demand of ultra-high count (over 1000 fibers) fiber optic cable for interconnection between each data center and distribution cable for inside of the data center have also increased. As a response to the current global market situation, high density fiber optic cables with flame retardant and low smoke zero halogen (LSZH) properties have been developed by applying spider web ribbons (SWR) and wrapping tube cable (WTC) technologies. In general, there are some differences for flame retardant tests by each country or region. For example, in Europe, the cable must comply with Construction Product Regulation with several different fire class requirements whereas in North America additionally it must comply with UL/CSA's O FNR indoor application requirement. [ABSTRACT FROM AUTHOR]

New vehicles are big sources of data and the massive adoption of the CANBUS allows capturing vehicles and drivers' behaviors on a day to day basis in real conditions. The performance of mobile internet networks allows to gather that information in data centers and creates new opportunities to optimize tire design and associated costs. At Michelin R&D center we developed a fully integrated system from data capture to a collaborative post-processing platform. There are technical and human challenges to setup such new design way. In this paper, after a description of the solution, we will present the different successes and challenges we faced during the last four years. [ABSTRACT FROM AUTHOR]

The article addresses government trends that affect records management in Australia. Topics covered include the Australian Government policy on digital transition in 2011 and its role in obliging government agencies in switching to digital information management in 4 years starting in 2011, ongoing usage of paper for recording public sector data as proven by the 2010 National Archives of Australia poll, and a project assessing present data archiving methods used by records professionals.

The article focuses on the importance of the paperless law firms in saving money and in increasing the efficiency. Topics discussed include electronic storage of data, importance of cloud computing in the management of the paperless office and importance of Drop box in file storage. It further discusses the hardware and software used and also the assurance of data protection with the cloud technology.

The article offers information on the IWCS Cable & Connectivity Symposium held in October 2018 in Providence, Rhode Island. Topics include technical innovations to advance connectivity solutions, technology trends and their impact on data center and network connectivity, use of artificial intelligence, and drivers for more bandwidth and for Microsoft platforms.

• This paper propose a PRE based trusted authorization scheme for nodes on CloudIoT. • It can update the authorization assuredly when some nodes are hijacked. • In this scheme, cloud servers are fully used than the other work. • The analysis shows it has better efficiency than other works with the same properties. In CloudIoT platform, the data is collected and shared by different nodes of Internet of Things (IoT), and data is processed and stored based on cloud servers. It has increased the abilities of IoT on information computation. Meanwhile, it also has enriched the resource in cloud and improved integration of the Internet and human world. All of this offer advantages as well as the new challenges of information security and privacy protection. As the energy limitation of the nodes in IoT, they are particularly vulnerable. It is much easier to hijack the nodes than to attack the data center for hackers. Thus, it is a crucial and urgent issue to realize the trusted update of authorization of nodes. When some nodes are hijacked, both of the behaviors to upload data to servers and to download information from servers should be forbidden. Otherwise, it might cause the serious damage to the sensitive data and privacy of servers. In order to solve this problem, we proposed a Proxy Re-encryption based Trusted Authorization scheme for nodes on CloudIoT (PRTA). PRTA is based on the proxy re-encryption (PRE), and the cloud server will play the roles of data storing and re-encrypting, which would reach the full potential of cloud computing and reduce the cost of nodes. The node's status is taken as one of the parameters for data re-encryption and it is under the authorization servers' control, which could ensure the security and reliability of the data and be beneficial for the privacy protection in CloudIoT. Also, the authorization servers are divided into the downloading and uploading kinds, which will make the application range much wider. [ABSTRACT FROM AUTHOR]

In this paper, we first develop some properties to state the relationships among central moments, stochastic dominance (SD), risk-seeking stochastic dominance (RSD), and integrals for the general utility functions and the polynomial utility functions of both risk averters and risk seekers. We then introduce the moment rule and establish some necessary and/or sufficient conditions between stochastic dominance and the moment rule for the general utility functions and the polynomial utility functions of both risk averters and risk seekers without imposing the same-location-scale-family condition. Thereafter, we apply the moment rules to develop some properties of portfolio diversification for the general utility functions and the polynomial utility functions for both risk averters and risk seekers. The findings in our paper enable academics and practitioners to draw preferences of both risk averters and risk seekers on their choices of portfolios or assets by using different moments. We illustrate this by using the moment rule tests to compare excess return of 49 industry portfolios from Kenneth French's online data library. • We develop some properties for the relationships among central moments, stochastic dominance (SD), risk-seeking SD, and integrals. • We introduce the moment rule and establish the necessary and/or sufficient conditions between SD and the moment rule for the general and polynomial utilities • We develop some properties of portfolio diversification for the general and polynomial utilities for both risk averters and risk seekers. • We illustrate the moment rule tests to compare excess return of 49 industry portfolios from Kenneth French''s online data library • The findings in our paper enable academics and practitioners to draw preferences on their choices of the portfolios or assets by using different moments. [ABSTRACT FROM AUTHOR]

Abstract: The cloud computing data center has numerous hosts as well as application requests. It needs to allocate resources dynamically according to the varied demands of users. It should not only provide QoS such as short response time and high throughput but also achieve efficient power consumption. This paper first puts forward a reconfiguration framework based on a request prediction, which anticipates the application request volume in advance. To determine the objective of relatively optimal configuration, it can work out the allocation scheme which can improve the resource utilization ratio as well as lower energy consumption. In addition, a concept of Utility Ratio Matrix (URM) is put forward to represent allocations of hosts and Virtual Machines (VMs), and a reconfiguration algorithm based on request prediction is also presented. The algorithm will predict the application requests so as to work out the allocation scheme in advance. The algorithm can separate the reconfiguration computing from the real allocation so that it can avoid a time delay between the reconfiguration result and the varied demands, and can also reduce the energy consumption in data center. The corresponding analysis and experimental results indicate the feasibility of the reconfiguration algorithm in this paper. [Copyright &y& Elsevier]

• Collective Opportunistic IoT Services are key abstractions for novel Internet- and Edge-of-Things applications. • IoT Modelling may consider entire collectives and various infrastructural elements as first-class entities. • Services implemented with Aggregate Computing can run in Edge or Cloudbased deployments, providing different pros and cons. Technological advances have recently fostered the Internet of Things vision, in which systems of situated entities perceive and act upon the world, and interact with one another to provide novel kinds of services, which are inherently cyber-physical, increasingly contextual and opportunistic in nature, and possibly span different scales and domains. The requirements of such IoT applications, however, pose significant non/functional challenges to engineering efforts, mitigated by emerging computing paradigms. On the infrastructure side, Cloud, Fog, and Edge Computing provide virtualised, on-demand, elastic resource provisioning – at the distant data centres, Network core and Edge – supporting the abstraction and scalability needs of IoT settings while also altogether giving options for QoS-driven trade-offs. However, despite intense research in these fields, there is still a gap of approaches supporting the engineering of dynamic, heterogeneous smart environments, such as those involving "collectives" of devices coordinating in a complex fashion to provide "global" services. In this paper, we integrate the Aggregate Computing and Opportunistic IoT Service models and propose a full-fledged approach for the engineering – from analysis to simulation – of complex "Edge of Things" applications. We compare by simulation two deployment targets for the same collective application: one centralised/Cloud-based, and the other decentralised/Edge-based. We discuss the trade-offs each one introduces, and we draw recommendations on application-driven choices of the appropriate deployment. [ABSTRACT FROM AUTHOR]

The article presents suggestions on preparing genealogy research logs. It refers to the significance of an electronic research log. It recommends to record the date and time or research. It advises to create source citations to maintain key details of the research. It suggests to tag each research log entry with keywords.

Abstract With the prevalence of cloud computing, the resource constrained clients are trended to outsource their computation-intensive tasks to the cloud server. Although outsourcing computation paradigm brings many benefits for both clients and cloud server, it causes some security challenges. In this paper, we focus on the outsourcing computation of matrix multiplication, and propose a new publicly verifiable computation scheme for batch matrix multiplication. Different from traditional matrix computation outsourcing model, the outsourcing task of our scheme is to compute MX i for group of clients, where X i is a private matrix chosen by different clients and M is a public matrix given by a data center beforehand. Based on the two techniques of privacy-preserving matrix transformation and matrix digest, our scheme can protect the secrecy of the client's private matrix X i and dramatically reduce the computation cost in both the key generation and the computing phases. Security analysis shows that the proposed scheme can achieve the desired security properties under the co-computational Diffie-Hellman assumption. We also provide the experimental evaluation that demonstrates the efficiency of our scheme. [ABSTRACT FROM AUTHOR]

Abstract: View materialization is an effective method to increase query efficiency in a data warehouse and improve OLAP query performance. However, one encounters the problem of space insufficiency if all possible views are materialized in advance. Reducing query time by means of selecting a proper set of materialized views with a lower cost is crucial for efficient data warehousing. In addition, the costs of data warehouse creation, query, and maintenance have to be taken into account while views are materialized. In this paper, we propose efficient algorithms to select a proper set of materialized views, constrained by storage and cost considerations, to help speed up the entire data warehousing process. We derive a cost model for data warehouse query and maintenance as well as efficient view selection algorithms that effectively exploit the gain and loss metrics. The main contribution of our paper is to speed up the selection process of materialized views. Concurrently, this will greatly reduce the overall cost of data warehouse query and maintenance. [Copyright &y& Elsevier]

The article reports on the launch of China's first Internet data center (IDC) optical transport that supports transport-software defined networking (T-SDN) and was built by telecommunication equipment company Huawei and search engine firm Baidu. Topics include the role of Huawei's T-SDN bandwidth on demand feature in providing transmission pipes for Baidu to handle burst traffic between IDCs, and the IDC optical transport network solutions built to help Baidu explore network operating modes.

Abstract With rapid development of cloud computing technologies and applications, the number and scale of cloud data centers have grown exponentially in recent years. One of the major problems with current cloud data centers is their huge energy consumption, which makes energy consumption management one of the hottest research topics in the field of cloud computing. This paper aims at implementing an effective Distributed Energy Meter (DEM) system for heterogeneous cloud environments based on a multi-component power consumption model for cloud servers. Specifically, we propose a modeling method for the energy consumption of key components (CPU, memory and disk) of computer servers and reveal the mathematical relationship between the resource usage of the key components and the system energy consumption. The proposed DEM system cannot only estimate the energy consumption of heterogeneous cluster environments (Linux and Windows NT), but also support various CPU power consumption models. In addition, a unique disk power consumption model that uses different thresholds to distinguish various disk I/O states (sequential/random, read/write) to achieve an accurate estimation of disk power consumption. Experimental studies conducted on a heterogeneous cluster with workloads generated by PCMark and Sysbench demonstrate that the proposed DEM system outperforms the state-of-art models in estimating the energy consumption of heterogeneous cloud environments. [ABSTRACT FROM AUTHOR]

The article discusses the highlights of the 2015 International Wire and Cable Symposium, held at the Hyatt Regency Atlanta in Georgia. Topics discussed presentations include power over communications cable, global codes and standards, data centers and micro-cables, the optical fiber outlook for 2016, insulated copper wire and cable prices and market, and Internet of Things.

The article offers information on the collaboration between Altera and Microsoft on software defined data center technology. Topics include field-programmable gate arrays (FPGAs), the paper titled "A Reconfigurable Fabric for Accelerating Large-Scale Data Center Services," and the high-performance HyperFlex architecture of Altera. It also discusses the collaboration of Altera and Intel to develop multi-die devices for programmable logic technology and package and assembly capabilities.

An introduction is presented to the article "VL2: A Scalable and Flexible Data Center Network" by Albert Greenberg, James R. Hamilton, Navendu Jain, Srikanth Kandula, Changhoon Kim, Parantap Lahiri, David A. Maltz, Parveen Patel, and Sudipta Sengupta, published elsewhere in the same issue.

A study from McMaster University discusses the potential bias in Artificial Intelligence (AI) algorithms used in cardiovascular care. The research highlights how bias can impact diagnoses, disease classification, risk prediction, and treatment recommendations, particularly affecting marginalized groups. The study proposes strategies to mitigate bias in AI algorithms to ensure equitable benefits for all individuals at risk for or living with cardiovascular disease. For more information, readers can refer to the article "Mitigating the Risk of Artificial Intelligence Bias In Cardiovascular Care" published in The Lancet Digital Health. [Extracted from the article]

The University of Central Florida conducted research on Lithium-Ion batteries used in electric vehicles, eVTOL aircraft, and drones. The study focused on battery aging, durability, and performance under various load conditions. The data set created by the research aims to provide valuable information for developing models for state-of-charge and state-of-health prognosis. The research findings will be available on the NASA Ames Prognostics Center of Excellence Data Repository for further study. [Extracted from the article]

The article focuses on the use of battery monitoring system (BMS) for reducing battery replacement costs, ensuring availability and enhancing the operational and worker safety. It informs on setting up the BMS for conditions such as temperature, and voltage; reviewing data on a regular basis; and upgrading a data centre.

The article reports on the rising water need of technology companies and their data centers for use across their operations. It suggests the role of data centers in water scarcity as droughts become longer and as data centers get more energy efficient. It informs about the investment by Microsoft in research to make systems more sustainable and efficient, the water stewardship pledges of Amazon Web Services, Google and Microsoft, and the plan of Meta for hydrological restoration projects.