Distributed Applications Research Articles

Optimal Sensor Scheduling Under Intermittent Observations Subject to Network Dynamics

Motivated by various distributed control applications, we consider a linear system with Gaussian noise observed by multiple sensors which transmit measurements over a dynamic lossy network. We characterize the stationary optimal sensor scheduling policy for the finite horizon, discounted, and long-term average cost problems and show that the value iteration algorithm converges to a solution of the average cost problem. We further show that the suboptimal policies provided by the rolling horizon truncation of the value iteration also guarantee stability and provide near-optimal average cost. Lastly, we provide qualitative characterizations of the multidimensional set of measurement loss rates for which the system is stabilizable for a static network, thus extending earlier results on intermittent observations.

Entanglement Swapping and Swapped Entanglement

Entanglement swapping is gaining widespread attention due to its application in entanglement distribution among different parts of quantum appliances. We investigate the entanglement swapping for pure and noisy systems, and argue different entanglement quantifiers for quantum states. We explore the relationship between the entanglement of initial states and the average entanglement of final states in terms of concurrence and negativity. We find that if initial quantum states are maximally entangled and we make measurements in the Bell basis, then average concurrence and average negativity of final states give similar results. In this case, we simply obtain the average concurrence (average negativity) of the final states by taking the product of concurrences (negativities) of the initial states. However, the measurement in non-maximally entangled basis during entanglement swapping degrades the average swapped entanglement. Further, the product of the entanglement of the initial mixed states provides an upper bound to the average swapped entanglement of final states obtained after entanglement swapping. The negativity work well for weak entangled noisy states but concurrence gives better results for relatively strong entanglement regimes. We also discuss how successfully the output state can be used as a channel for the teleportation of an unknown qubit.

Algorithms optimize circle packing in regular polygons

The complex geometrical problem of packing has applications in charge distribution and granular matter.

Experimental free-space continuous-variable quantum key distribution with thermal source.

Passive-state-preparation (PSP) continuous-variable quantum key distribution (CVQKD) protocol explores the intrinsic field fluctuations of a thermal source. Compared with traditional Gaussian-modulated coherent-state CVQKD, it does not need active modulations and has promising applications in chip integration and portable free-space quantum key distribution. In this Letter, we propose and experimentally realize a PSP CVQKD scheme with transmitted local oscillator (LO) through fluctuating transmittance free-space channel using an off-the-shelf amplified spontaneous emission source for the first time. By proposing thermal-state polarization multiplexing transmitted LO, synchronized channel transmittance monitoring and fine-grained phase compensation techniques, secure keys within -15 dB transmittance of simulated free-space channel with turbulence are generated, with a final average secure key rate of 1.015 Mbps asymptotically. Equivalent atmospheric turbulence model analysis shows that the free-space PSP CVQKD scheme provides a promising outlook for high-speed and chip-based CVQKD for kilometer-level atmospheric channel networks.

Pentose-Fermenting Yeasts in Nature: Ecology, Biodiversity and Applications

The world’s energy sector has undergone drastic changes in the last decades due to the climate change and the turn to renewable energy sources. Biomass is the largest renewable source of carbohydrates on the Earth and is an important substrate for biofuel production. Saccharomyces cerevisiae yeasts are the main producer of first-generation ethanol from corn and sugarcane. However, these yeasts lack the ability to utilize the important components of lignocelluloses such as pentoses D-xylose and L-arabinose. Pentose-fermenting yeasts could become an alternative to S. cerevisiae in ethanol production from lignocelluloses. This review focuses on the ecology, geographical distribution, taxonomy, and potential applications of naturally-occurring pentose-fermenting yeasts. Pentose-fermenting yeasts have been frequently found in the lignocelluloseassociated substrates. Decaying and rotten wood and the gut of wood-boring insects are especially important natural reservoirs of this group of yeasts. Simple sugars xylose and L-arabinose would be present in such habitats as suitable nutrients for pentose-assimilating yeasts. The other natural habitats reported for pentose-fermenting yeasts are soil, plants, and herbivore faeces. Pentose-fermenting yeasts are found in many geographical regions and have been isolated on almost each continent. Dozens of novel pentose-fermenting yeast species have been discovered in the last decade. The previously poorly explored regions, including Brazil, China, and several Asian countries were especially oft en reported as sites of isolation of such yeasts. Most xylose-fermenting yeasts belong to genera Scheffersomyces, Candida, Spathaspora, Sugiyamaella, and Pachysolen, while the most efficient ethanol producers are represented by species Scheffersomyces stipitis and Spathaspora passalidarum. The vast majority of research on the biotechnological application of pentose-fermenting yeasts focuses on their role in the production of bioethanol from lignocellulose. This group of yeasts could be either directly involved in the fermentation stage of ethanol production or serve as a source of genetic material for the genetic manipulation of other industrial yeast strains. Pentose-fermenting yeasts could also be involved in the production of various chemicals from lignocellulosic substrates, mainly polyols, xylitol, and arabitol. Thus, the search for novel pentose-fermenting yeasts that could become new efficient ethanol producers or donors of new genetic material is still ongoing. The previously unexplored or poorly studied geographical regions and natural habitats can hide many novel yeasts with huge biotechnological potential.

A hybrid moth–flame algorithm with particle swarm optimization with application in power transmission and distribution

The transmission lines are used for power distribution across large distances. Different parameters affect the power transmission efficiency, and the quality of service. Furthermore, the transmission system parameter estimation is crucial for power flow analysis, electric power system expansion planning, stability, dispatch, and economic analysis. This task is created by utilizing system identification techniques, and with the analytical method being the most commonly utilized methodology for acquiring transmission line data. However, to address an issue that simplifies the estimation these techniques have significant downsides — such as non-recursive parameter estimation and the accessibility of an appropriately transposed line. This paper presents a hybrid moth-flame optimization (MFO) and particle swarm optimization (PSO) for estimating transmission line parameters based on various scenarios and mathematical validation on different benchmark functions. The concepts of MFO and PSO are rationally integrated into this algorithm to overcome their limitations and improve their global search ability. Regarding solution quality and convergence speed the results show that the proposed hybrid algorithm performs better than the conventional PSO and original MFO.

Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey

Intelligent energy management in renewable-based power distribution applications, such as microgrids, smart grids, smart buildings, and EV systems, is becoming increasingly important in the context of the transition toward the decentralization, digitalization, and decarbonization of energy networks. Arguably, many challenges can be overcome, and benefits leveraged, in this transition by the adoption of intelligent autonomous computer-based decision-making through the introduction of smart technologies, specifically artificial intelligence. Unlike other numerical or soft computing optimization methods, the control based on artificial intelligence allows the decentralized power units to collaborate in making the best decision of fulfilling the administrator’s needs, rather than only a primitive decentralization based only on the division of tasks. Among the smart approaches, reinforcement learning stands as the most relevant and successful, particularly in power distribution management applications. The reason is it does not need an accurate model for attaining an optimized solution regarding the interaction with the environment. Accordingly, there is an ongoing need to accomplish a clear, up-to-date, vision of the development level, especially with the lack of recent comprehensive detailed reviews of this vitally important research field. Therefore, this paper fulfills the need and presents a comprehensive review of the state-of-the-art successful and distinguished intelligent control strategies-based RL in optimizing the management of power flow and distribution. Wherein extensive importance is given to the classification of the literature on emerging strategies, the proposals based on RL multiagent, and the multiagent primary secondary control of managing power flow in micro and smart grids, particularly the energy storage. As a result, 126 of the most relevant, recent, and non-incremental have been reviewed and put into relevant categories. Furthermore, salient features have been identified of the major positive and negative, of each selection.

Meeting Coflow Deadlines in Data Center Networks With Policy-Based Selective Completion

Recently, the abstraction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coflow</i> is introduced to capture the collective data transmission patterns among modern distributed data-parallel applications. During processing, coflows generally act as barriers; accordingly, time-sensitive applications prefer their coflows to complete within deadlines, and deadline-aware coflow scheduling becomes very crucial. Regarding these data-parallel applications, we notice that many of them, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">large-scale query systems</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed iterative training</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">erasure codes enabled storage</i> , are able to tolerate loss-bounded incomplete inputs by design. This tolerance indeed brings a flexible design space for the schedule of their coflows: when getting overloaded, the network can trade coflow completeness for the timeliness, and balance the completeness of different coflows on demand. Unfortunately, existing coflow schedulers neglect this tolerance, resulting in inflexible and inefficient bandwidth allocations. In this paper, we explore this fundamental trade-off and design POCO, a POlicy-based COflow scheduler, along with a transport layer enhancement scheme, to achieve customizable selective coflow completion for emerging time-sensitive distributed applications. Internally, POCO employs a suite of novel designs along with admission controls to make <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flexible</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">work-conserving</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">performance-guaranteed</i> rate allocation to online coflow requests very efficiently. Extensive trace-based simulations indicate that POCO is highly flexible and achieves optimal coflow schedules respecting the requirements specified by applications.

Double-sided injection lap riveting

This article presents a double-sided injection lap riveting process for fixing two overlapped sheets with tubular rivets at room temperature. The rivets are injected by compression into the dovetail ring holes that are previously machined in both sheets, and, in contrast to other joining by plastic deformation processes making use of auxiliary elements, the resulting joints are hidden inside the sheets without material protrusions above or below their surfaces. The new process is applied in the fabrication of aluminum busbar joints for energy distribution systems, and comparisons are made against conventional bolted joints that were fabricated for reference purposes. The work combines experimentation and finite element modelling, and results allow concluding that, in addition to invisibility and savings in assembly space, there are important gains in the thermo-electrical performance of the new joints that are of paramount importance for electric distribution applications.

Establishment and Application of Spatial Distribution Model of Roof Greening Based on GIS Technology

The stage from the beginning of the 21st century to now is the stage of the scale development of roof greening(RG) in China. With the continuous progress of urban construction, on the one hand, the ecological environment is deteriorating, on the other hand, the available green space in the urban center area becomes more and more scarce. The development of this contradiction promotes the rapid development of RG. RG plays an important role in enriching urban landscape, increasing urban green quantity, reducing urban heat island effect, conserving rainwater and reducing temperature, and creating a new green space for the city, including leisure, entertainment and ecology, and improving urban ecological landscape. This paper mainly studies the establishment and application of spatial distribution(SD) model of RG Based on GIS technology. Through understanding the SD design of RG, the paper investigates and analyzes the status quo of green space of roof, macro layout and the integration of multiple functional spaces. This paper studies and establishes a multiobjective optimization model of SD of RG, and analyzes the purification effect of RG on rainwater pollutants and the overall performance of ecosystem service equity. The results show that 26.31% of the people think that the degree of RG is insufficient, and 5.14% think that RG is scarce. In the cognition of the status of RG space, 12.41% of people don't care about RG, 8.69% think it is face engineering, and even 9.31% think it is waste of resources, which indicates that the promotion of RG needs to be improved.

A New Power Topp-Leone distribution with applications to engineering and industry data.

We introduced a brand-new member of the family that is going to be referred to as the New Power Topp-Leone Generated (NPTL-G). This new member is one of a kind. Given the major functions that created this new member, important mathematical aspects are discussed in as much detail as possible. We derived some functions for the new one, included the Rényi entropy, the qf, series development, and moment weighted probabilities. Moreover, to estimate the values of the parameters of our model that were not known, we employed the maximum likelihood technique. In addition, two actual datasets from the real world were investigated in order to bring attention to the possible applications of this novel distribution. This new model performs better than three key rivals based on the measurements that were collected.

Spatial distribution analysis and application of engineering disturbance disasters in the Himalayan alpine valley

The Himalayan alpine canyon area is characterized by complex engineering geological conditions and abnormal internal and external dynamic geological processes. Severe slope disturbance disasters can be caused by engineering disturbances. In this study, field investigations and theoretical analyses were performed to determine the formation mechanism, spatial distribution law, and controlling factors of engineering disturbance disasters in the Himalayan alpine and canyon areas. A total of 396 engineering disturbance disasters were identified within the scope of the 2,800-km survey line. A geographic information system and mathematical statistical analysis were used to analyze the correlation between engineering disturbance disasters and factors such as the slope, slope aspect, elevation, peak ground acceleration, distance from fault, distance from river, rainfall, lithological changes, and historical earthquake effects. The statistical analysis indicates a good power-law and exponential distribution between the engineering disturbance disaster concentration and the slope and distance from the river, respectively. The slope and distance from the river are the two most important factors in determining the spatial distribution of engineering disturbance disasters; the other factors also influence the distribution to some extent. These factors affect the quality of the slope rock and soil mass, affecting slope stability. The main form of engineering disturbance in the study area is slope cutting. The direct result (increase in slope) and secondary result (decrease in rock mass quality caused by unloading rebound) of slope cutting are the most important factors inducing engineering disturbance disasters. Based on previous research results, factors in engineering disturbance disasters in alpine and canyon areas were evaluated, and the distribution of disturbance disasters along the China–Nepal Railway was predicted. The study area was divided into extremely high-(13.6%), high-(30.4%), medium-(34.1%), and low-susceptibility (22.0%) areas. The research results can provide a theoretical basis for prevention and treatment of engineering disturbance disasters in Himalayan alpine valley areas.

Corydalis saxicola Bunting: A Review of Its Traditional Uses, Phytochemistry, Pharmacology, and Clinical Applications.

Corydalis saxicola Bunting (CSB), whose common name in Chinese is Yanhuanglian, is a herb in the family Papaveraceae. When applied in traditional Chinese medicine, it is used to treat various diseases including hepatitis, abdominal pain, and bleeding haemorrhoids. In addition, Corydalis saxicola Bunting injection (CSBI) is widely used against acute and chronic hepatitis. This review aims to provide up-to-date information on the botanical distribution, description, traditional uses, phytochemistry, pharmacology, and clinical applications of CSB. A comprehensive review was implemented on studies about CSB from several scientific databases, such as SciFinder, Elsevier, Springer, ACS Publications, Baidu Scholar, CNKI, and Wanfang Data. Phytochemical studies showed that 81 chemical constituents have been isolated and identified from CSB, most of which are alkaloids. This situation indicates that these alkaloids would be the main bioactive substances and that they have antitumour, liver protective, antiviral, and antibacterial pharmacological activities. CSBI can not only treat hepatitis and liver cancer but can also be used in combination with other drugs. However, the relationships between the traditional uses and modern pharmacological actions, the action mechanisms, quality standards, and the material basis need to be implemented in the future. Moreover, the pharmacokinetics of CSBI in vivo and the toxicology should be further investigated.

ModularFed: Leveraging modularity in federated learning frameworks

Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms. However, the standards of the available frameworks can no longer sustain the rapid advancement and hinder the integration of FL solutions, which can be prominent in advancing the field. In this paper, we propose ModularFed, a research-focused framework that addresses the complexity of FL implementations and the lack of adaptability and extendability in the available frameworks. We provide a comprehensive architecture that assists FL approaches through well-defined protocols covering three dominant FL paradigms: adaptable workflow, datasets distribution, and third-party application support. Within this architecture, protocols are blueprints that strictly define the framework’s components’ design, contribute to its flexibility, and strengthen its infrastructure. Further, our protocols aim to enable modularity in FL, supporting third-party plug-and-play architecture and dynamic simulators coupled with major built-in data distributors. Additionally, the framework support wrapping multiple approaches in a single environment to enable consistent replication of FL issues such as clients’ deficiency, data distribution, and network latency, which entails a fair comparison of techniques outlying FL technologies. In our evaluation, we examine the applicability of our framework addressing major FL domains, including statistical distribution and modular-based resource monitoring tools and client selection. Moreover, our comparison analysis indicates that our architecture has an inconsiderable impact on performance compared to other approaches.

Three-dimensional reduced graphene reinforced cement with enhanced safety and durability for drinking water distribution applications: Long-term experimental and theoretical study

Cement mortar lining (CML) is commonly used for iron pipe internal corrosion inhibition in drinking water distribution system (DWDS), however, the corrosion of CML itself is still a problem, particularly under soft water conditions. In this study, both long-term experimental study and theoretical studies were conducted to evaluate the effects of graphene oxide (GO) and three-dimensional reduced graphene oxide (3D-rGO) as additives on the stability of CML and the corresponding water quality. Results showed that during a 182-day leaching experiment, the 3D-rGO modified cement had a higher ability to inhibit the cement constituent leaching than GO modified and original cements. Structural characterization indicated that the addition of 3D-rGO could slightly promote the degree of calcium hydroxide crystallization in CML. Molecular dynamics simulation demonstrated that the 3D-rGO nanosheets strengthened the tensile strain of the cement and restricted the movement of calcium ions by forming strong bonds with the calcium-silicate-hydrate gel network. In addition, compared with GO modified cement and original cement, the 3D-rGO modified cement could somewhat reduce the disinfection by-products formation and the microbial richness in drinking water. Thus, the reinforcement of cement by 3D-rGO could enhance the safety and durability of CML iron pipes in DWDS.

Management of artesian wells at bumi purnawira housing sukabumi city with water distribution application

The problem of clean water supply to meet the needs of the community is the main obstacle experienced by residents of Bumi Purnawira housing. Water management is completely left to the residents starting from installation of pipes to each house, installation of water meters, calculation of the cost of water usage per cubic meter, as well as administration and maintenance of the artesian well. Some problem that arise include: frequent miscalculation of cost to be paid; monthly meter recapitulation records in the form of paper prone to damage and data lost; meter recapitulation records is only kept by the treasurer, so if the residents want to know the history of meter records and the payment, they have to see the treasurer; the meter’s monthly status is vulnerable to manipulation whether done by the residents or the treasurer; There is often a discrepancy between the financial recapitulation recorded and the physical cash; and financial expenditures are sometimes not properly recorded, so that it triggers the discrepancy between the recorded amount and the physical cash. Seeing the existing reality, an idea to make an application for the management of artesian well administration has arisen. The initial step is to form two teams, namely the development team and the socialization team. The development team is responsible for developing the management system of the artesian well’s administration and finance. Meanwhile, the socialization team is responsible for conducting the socialization and the application training. The result of this project is an application and socialization that can help both the administrators and the residents in managing the administration and finance of the artesian well.

Low-power multi-cloud deployment of large distributed service applications with response-time constraints

Distributed service applications make heavy use of clouds and multi-clouds, and must (i) meet service quality goals (e.g. response time) while (ii) satisfying cloud resource constraints and (iii) conserving power. Deployment algorithms must (iv) provide a solution meeting these requirements within a short time to be useful in practice. Very few existing deployment methods address the first three requirements, and those that do take too long to find a deployment. The Low-Power Multi-Cloud Application Deployment (LPD) algorithm fills this gap with a low-complexity heuristic combination of generalized graph partitioning between clouds, bin-packing within each cloud and queueing approximations to control the response time. LPD has no known competitor that quickly finds a solution that satisfies response time bounds. A host execution time approximation for contention is fundamental to achieving sufficient solution speed. LPD is intended for use by cloud managers who must simultaneously manage hosts and application deployments and plan capacity to offer services such as Serverless Computing.On 104 test scenarios deploying up to 200 processes with up to 240 replicas (for scaling), LPD always produced a feasible solution within 100 s (within 20 seconds in over three-quarters of cases). Compared to the Mixed Integer Program solution by CPLEX (which took a lot longer and was sometimes not found) LPD solutions gave power consumption equal to MIP in a third of cases and within 6% of MIP in 95% of cases. In 93% of all 104 cases the power consumption is within 20% of an (unachievable) lower bound.LPD is intended as a stand-alone heuristic to meet solution time restrictions, but could easily be adapted for use as a repair mechanism in a Genetic Algorithm.

Validation of a strain gauge rosette setup on a cantilever specimen: Application to a calibration bench for residual stresses

It is commonly known that the most difficult part of measuring residual stresses through diffraction or relaxation methods is the high sensitivity of the results to input errors, such as noise in the strain data. Then, quantifying and minimizing stress uncertainties is at least as important as the residual stress results themselves. Results are often validated by leveraging different measurement techniques, although each method is somehow specialized at detecting residual stresses at different locations and length scales. This leads to a fundamental lack of ground truth data and an inherent difficulty in detecting biases.The authors have introduced a calibration bench that facilitates the application of a well-known bending stress distribution on a specimen while conducting residual stress measurements using either the Hole-Drilling Method (HDM) or X-ray Diffraction (XRD). By leveraging Bueckner's superposition principle, the bench allows for determination of both the residual stress distribution and the reference stress distribution through a single experimental setup. This approach not only enables direct evaluation of accuracy but also identification of any procedural systematic errors, as the reference stress distribution is known with a high degree of certainty.In this work, a detailed characterization of the stress and strain fields generated by the externally applied load was pursued. Then, the calibration bench was used to perform a validated characterization of residual stresses produced by two shot peening treatments, through both XRD and HDM. Additionally, both techniques were employed to verify the recognized bending stresses, thereby validating the findings of the residual stress measurements.

Automation of the Application of Data Distribution with Overlapping in Distributed Memory

Automation of the Application of Data Distribution with Overlapping in Distributed Memory

An application of Pascal distribution involving Kamali type related to leaf like domain

&lt;abstract&gt;&lt;p&gt;This paper aims to study the Geometric properties of analytic function in the open unit disk. In the present investigation, we obtain some geometric properties of Pascal distribution involving Kamali type related to leaf like domain. In this paper, we find coefficient inequality, Radii Properties, convolution product, partial sum of the class $ \Sigma(\delta, \Phi, \beta, s, t, m) $. Furthermore, we examine the distortion bounds belonging to the same class.&lt;/p&gt;&lt;/abstract&gt;