Integrated Blockchain and Cloud Computing Systems: A Systematic Survey, Solutions, and Challenges

Cloud computing is a network model of on-demand access for sharing configurable computing resource pools. Compared with conventional service architectures, cloud computing introduces new security challenges in secure service management and control, privacy protection, data integrity protection in distributed databases, data backup, and synchronization. Blockchain can be leveraged to address these challenges, partly due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. We present a comprehensive survey of how blockchain is applied to provide security services in the cloud computing model and we analyze the research trends of blockchain-related techniques in current cloud computing models. During the reviewing, we also briefly investigate how cloud computing can affect blockchain, especially about the performance improvements that cloud computing can provide for the blockchain. Our contributions include the following: (i) summarizing the possible architectures and models of the integration of blockchain and cloud computing and the roles of cloud computing in blockchain; (ii) classifying and discussing recent, relevant works based on different blockchain-based security services in the cloud computing model; (iii) simply investigating what improvements cloud computing can provide for the blockchain; (iv) introducing the current development status of the industry/major cloud providers in the direction of combining cloud and blockchain; (v) analyzing the main barriers and challenges of integrated blockchain and cloud computing systems; and (vi) providing recommendations for future research and improvement on the integration of blockchain and cloud systems.

Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective

Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.

Investigation of Suction Flow Control on Centrifugal Compressor with Vaned Diffuser

In this study, the effect of suction flow control on a centrifugal compressor at operation and stall flow rates was investigated using computational fluid dynamics (CFD). The compressor geometry was reconstructed from available open source profile data and the CFD analyses have been performed on this geometry using the appropriate mesh. To validate the CFD results, the compressor performance line was acquired and compared with the experimental results obtained at the design rotational speed. Then, suction flow control was employed at various suction slot positions with different suction flow rates to improve the performance of the compressor at operation and stall flow rates. As a result of the suction flow control trials, 0.85% increase in pressure ratio and 0.8% increase in adiabatic efficiency were achieved while the compressor was running at operation flow rate. The performance improvements corresponding to the stall flow rate of the compressor were 2.5% increase in pressure ratio and 2% increase in adiabatic efficiency.

The Effects of Exercise Therapy Moderated by Sex in Rehabilitation of COVID-19

BackgroundPulmonary rehabilitation serves as a key component in the recovery of COVID-19 and standardized exercise therapy programs in pulmonary rehabilitation have been shown to significantly improve physical performance and lung function parameters in post-acute COVID-19 patients. However, it has not been investigated if these positive effects are equally beneficial for both sexes, especially considering a more severe physical impact of COVID-19 in men when compared to women. Therefore, the purpose of this study was to analyze outcomes of a pulmonary rehabilitation program with respect to sex differences, in order to identify sex-specific pulmonary rehabilitation requirements.MethodsData of 233 patients (40.4% females) were analyzed before and after a three-week standardized pulmonary rehabilitation program. Patients were admitted to rehabilitation due to post-acute COVID-19 illness and staged using the COVID-19 Severity Scale by Huang et al. (2021). Lung function parameters were assessed as part of the clinical routine using spirometry (ICmax, maximal inspiratory capacity) and body plethysmography (FVC, forced vital capacity; FEV1, forced expiratory volume in the first second) and functional exercise capacity was measured by the Six-Minute Walk Test (6MWT). For the comparison of lung function and walking parameters by sex, Welch-ANOVA was used, as results of Levene's test suggested significant heteroscedasticity regarding the investigated parameters (p > 0.05). When comparing post-treatment 6MWT, FEV1 and FCV to corresponding reference values, paired t-tests were used.ResultsAt post-rehabilitation, ICmax, FVC, FEV1 and 6MWT has been improved in both sexes. Females showed a significantly smaller improvement in FEV1 and ICmax (F = 5.86, ω2 = .02; p < 0.05) than males. There was no statistically significant difference in FVC and 6MWT performance improvements between men and women. After the rehabilitation stay, females made greater progress towards reference values of 6MWT (T(231) = -3.04; p < 0.01) and FEV1 (T(231) = 2.83; p < 0.01) than males.ConclusionsSex differences in the improvement of lung function parameters seem to exist when completing a three-week pulmonary rehabilitation program and should be considered when personalizing standardized exercise therapies in pulmonary rehabilitation.Trial registrationthis study was registered in the German Clinical Trials Register (DRKS00026936) on 2021/10/19.

A flexible class of dependence-aware multi-label loss functions

The idea to exploit label dependencies for better prediction is at the core of methods for multi-label classification (MLC), and performance improvements are normally explained in this way. Surprisingly, however, there is no established methodology that allows to analyze the dependence-awareness of MLC algorithms. With that goal in mind, we introduce a class of loss functions that are able to capture the important aspect of label dependence. To this end, we leverage the mathematical framework of non-additive measures and integrals. Roughly speaking, a non-additive measure allows for modeling the importance of correct predictions of label subsets (instead of single labels), and thereby their impact on the overall evaluation, in a flexible way. The well-known Hamming and subset 0/1 losses are rather extreme special cases of this function class, which give full importance to single label sets or the entire label set, respectively. We present concrete instantiations of this class, which appear to be especially appealing from a modeling perspective. The assessment of multi-label classifiers in terms of these losses is illustrated in an empirical study, clearly showing their aptness at capturing label dependencies. Finally, while not being the main goal of this study, we also show some preliminary results on the minimization of this parametrized family of losses.

Analyzing the generalizability of the network-based topic emergence identification method

Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.

Enhancing strategic defensive positioning and performance in the outfield

Over the past 20 years, professional and collegiate baseball has undergone a transformation, with statistics and analytics increasingly factoring into most of the decisions being made on the field. One particular example of the increased role of analytics is in the positioning of outfielders, who are tasked with tracking down balls hit to the outfield to record outs and minimize potential offensive damage. This paper explores the potential of location analytics to enhance the strategic positioning of players, enabling improved response and performance. We implement a location optimization model to analyze collegiate ball-tracking data, seeking outfielder locations that simultaneously minimize the average distance to a batted ball and maximize the weighted importance of batted ball coverage within a response standard. Trade-off outfielder configurations are compared to observed fielder positioning, finding that location models and spatial optimization can lead to performance improvements ranging from 1 to 3%, offering a significant strategic advantage over the course of a season.

Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of predictive adaptive control and its application in the industrial environment. The proposed MIMO-APC (MIMO adaptive predictive controller) performance is validated with two simulated processes: a quadrotor drone and the quadruple tank process. In the first experiment the proposed MIMO APC shows ISE-IAE-ITAE performance indices improvements of up to 25%, 25.4% and 38.9%, respectively. For the quadruple tank process the water levels in the lower tanks follow closely the set points, with the exception of a 13% overshoot in tank 1 for the minimum phase behavior response. The controller responses show significant performance improvements when compared with previously published MIMO control strategies.

Implementation of the optimal construction of a combination device and evaluation of reliability by output voltage

The theoretical propositions of the algebra of logic are considered. It is noted that the current microcircuitry based on the algebra of logic contains logical statements: true (yes) is a logical unit, false (no) is a logical zero. Based on the given logical function: ((ABC)×D + A×(BCD) + A×(BC)×D + (AB)×(CD), frontal, minimal, transformed minimal variants of the combination device are implemented, as well as minimized variants in the bases "AND-NOT" and "OR-NOT". A combination device based on import-substituting chips of 155, 176 series has been designed. The analysis of the obtained devices is made from the standpoint of technical and economic indicators, in particular, an assessment of the number of logic elements used, an assessment of the symmetry of the structure, as a result, a reduction in energy consumption, an increase in performance, improvements in parameters for reliability of functioning, a decrease in weight and size characteristics. Assuming that the law of change of the information parameter U1 is close to linear, taking into account the effect of temperature as boundary values for the elements of the applied microcircuits, taking the values -60 °C and +120°. Accordingly, the parametric reliability of the optimal implementation of the device according to the output voltage parameter is calculated. The conclusion is made about the inverse dependence of parametric reliability on temperature growth. A recommendation is given when evaluating parametric reliability for a number of other information parameters about the need to take into account both the number of chips used and the type of their interconnections.

Multi-Variate Optimization of Polymer Electrolyte Membrane Fuel Cells in Consideration of Effects of GDL Compression and Intrusion

This study applies a comprehensive surrogate-based optimization techniques to optimize the performance of polymer electrolyte membrane fuel cells (PEMFCs). Parametric cases considering four variables are defined using latin hypercube sampling. Training and test data are generated using a multidimensional, two-phase PEMFC simulation model. Response surface approximation, radial basis neural network, and kriging surrogates are employed to construct objective functions for the PEMFC performance. There accuracies are tested and compared using root mean square error and adjusted R-square. Surrogates linked with optimization algorithms, i.e., genetic algorithm and particle swarm optimization are used to determine the optimal design points. Comparative study of these surrogates reveals that the kriging model outperforms the other models in terms of prediction capability. Furthermore, the PEMFC model simulations at the optimal design points demonstrate that performance improvements of around 56–69 mV at 2.0 A/cm2 are achieved with the optimal design compared to typical PEMFC design conditions.