A Novel Hybrid Approach for Multi-Dimensional Data Anonymization for Apache Spark

Multi-dimensional data anonymization approaches (e.g., Mondrian) ensure more fine-grained data privacy by providing a different anonymization strategy applied for each attribute. Many variations of multi-dimensional anonymization have been implemented on different distributed processing platforms (e.g., MapReduce, Spark) to take advantage of their scalability and parallelism supports. According to our critical analysis on overheads, either existing iteration-based or recursion-based approaches do not provide effective mechanisms for creating the optimal number of and relative size of resilient distributed datasets (RDDs), thus heavily suffer from performance overheads. To solve this issue, we propose a novel hybrid approach for effectively implementing a multi-dimensional data anonymization strategy (e.g., Mondrian) that is scalable and provides high-performance. Our hybrid approach provides a mechanism to create far fewer RDDs and smaller size partitions attached to each RDD than existing approaches. This optimal RDD creation and operations approach is critical for many multi-dimensional data anonymization applications that create tremendous execution complexity. The new mechanism in our proposed hybrid approach can dramatically reduce the critical overheads involved in re-computation cost, shuffle operations, message exchange, and cache management.

Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms

Proximal soil sensors are receiving strong attention from several disciplinary fields, and this has led to a rise in their availability in the market in the last two decades. The aim of this work was to validate agronomically a zone management delineation procedure from electromagnetic induction (EMI) maps applied to two different rainfed durum wheat fields. The k-means algorithm was applied based on the gap statistic index for the identification of the optimal number of management zones and their positions. Traditional statistical analysis was performed to detect significant differences in soil characteristics and crop response of each management zones. The procedure showed the presence of two management zones at both two sites under analysis, and it was agronomically validated by the significant difference in soil texture (+24.17%), bulk density (+6.46%), organic matter (+39.29%), organic carbon (+39.4%), total carbonates (+25.34%), total nitrogen (+30.14%), protein (+1.50%) and yield data (+1.07 t ha−1). Moreover, six unmanned aerial vehicle (UAV) flight missions were performed to investigate the relationship between five vegetation indexes and the EMI maps. The results suggest performing the multispectral images acquisition during the flowering phenological stages to attribute the crop spatial variability to different soil proprieties.

Contact Heat Evoked Potentials in China: Normal Values and Reproducibility

Background: Contact heat evoked potentials (CHEPs) is used to diagnose small fiber neuropathy (SFN). We established the normal values of CHEPs parameters in Chinese adults, optimized the test technique, and determined its reproducibility.Methods: We recruited 151 healthy adults (80 men; mean age, 37 ± 14 years). CHEPs was performed on the right forearm to determine the optimal number of stimuli, and then conducted at different sites to establish normal values, determine the effects of demographic characteristics and baseline temperature, and assess the short- (30 min) and long-term (1 year) reproducibility. N2 latency/height varied with age and sex, while P2 latency/height and N2–P2 amplitude varied with age. The optimal number of stimuli was three.Results: N2 latency/height (t = 5.45, P < 0.001) and P2 latency/height (χ2 = −4.06, P < 0.001) decreased and N2–P2 amplitude (t = −5.01, P < 0.001) and visual analog scale score (χ2 = −5.84, P < 0.001) increased with increased baseline temperature (35 vs. 32°C). CHEPs parameters did not differ with time (baseline vs. 30 min vs. 1 year).Conclusion: We established normal CHEPs values in Chinese adults. We found that CHEPs parameters changed with baseline temperature and that the short- and long-term test reproducibility were satisfactory.

FunOrder 2.0 – a fully automated method for the identification of co-evolved genes

Coevolution is an important biological process that shapes interacting species or even proteins – may it be physically interacting proteins or consecutive enzymes in a metabolic pathway. The detection of co-evolved proteins will contribute to a better understanding of biological systems. Previously, we developed a semi-automated method, termed FunOrder, for the detection of co-evolved genes from an input gene or protein set. We demonstrated the usability and applicability of FunOrder by identifying essential genes in biosynthetic gene clusters from different ascomycetes. A major drawback of this original method was the need for a manual assessment, which may create a user bias and prevents a high-throughput application. Here we present a fully automated version of this method termed FunOrder 2.0. To fully automatize the method, we used several mathematical indices to determine the optimal number of clusters in the FunOrder output, and a subsequent k-means clustering based on the first three principal components of a principal component analysis of the FunOrder output. Further, we replaced the BLAST with the DIAMOND tool, which enhanced speed and allows the future integration of larger proteome databases. The introduced changes slightly decreased the sensitivity of this method, which is outweighed by enhanced overall speed and specificity. Additionally, the changes lay the foundation for future high-throughput applications of FunOrder 2.0 in different phyla to solve different biological problems.

oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

Background When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated. Results This study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally. Conclusions oCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM.

Ti/TiO2/SiO2 multilayer thin films with enhanced spectral selectivity for optical narrow bandpass filters

Thin film-based optical sensors have been attracting increasing interest for use in developing technologies such as biometrics. Multilayered dielectric thin films with different refractive indices have been utilized to modulate the optical properties in specific wavelength bands for spectral selectivity of Thin Film Narrow Bandpass Filters (TFNBFs). Progress in TFNBF design has been made with the incorporation of metallic thin films. Narrower bandwidths with higher transmittance have been achieved in specific spectral bands. In this work, Ti/TiO2/SiO2 based multilayer thin films were prepared using pulsed-DC reactive sputtering. Computer simulations using the Essential Macleod Program allowed the optimal number of layers and thickness of the multilayer thin films to be determined to efficiently tailor the optical path transmitting specific wavelength bands. The addition of Ti metal layers within dielectric (TiO2/SiO2) multilayer thin films significantly changes the cutoff frequency of transmittance at specific wavelengths. Representative 26 multilayer films consisting of Ti, TiO2, and SiO2 show lower transmittance of 10.29% at 400 nm and 10.48% at 680 nm. High transmittance of 80.42% at 485 nm was observed, which is expected to improve the spectral selectivity of the TFNBF. This work provides a contribution to future simulation based design strategy based on experimental thin film engineering for potential industrial development opportunities such as optical biometrics.

Reducing total annual cost and CO2 emissions in batch distillation for separating ternary wide boiling mixtures using vapor recompression heat pump

This paper presents cost-effective heat pump assisted vapor recompression (VRC) design algorithms for the separation of ternary wide boiling mixture in batch distillation in order to reduce total annual cost (TAC) and carbon dioxide (CO2) emissions. A minimum TAC and CO2 is required by the batch distillation process industry for any investments in heat integrated systems, such as VRC. Consequently, the design conditions for implementing VRC should be chosen such that the energetic performance is maximum at minimum TAC. The model system selected in this paper is an application involving high temperature lift, that is, hexanol–octanol–decanol ternary wide boiling mixture. First, a systematic simulation algorithm was developed for conventional multicomponent batch distillation (CMBD) and single-stage vapor recompressed multicomponent batch distillation (SiVRMBD) to determine the optimal number of stages based on the maximum TAC savings. The SiVRMBD saves more energy and TAC than CMBD. However, SiVRMBD has a high compression ratio (CR) throughout the operation, which is not practically feasible for the batch distillation processing. Second, in order to increase the performance and minimize the SiVRMBD weakness, a novel optimal multi-stage vapor recompression algorithm was proposed to operate at the lowest possible CR (<3.5) throughout the batch operation while also conserving the most TAC. Overall, the findings suggest that the proposed optimal multi-stage VRC reduces TAC and CO2 emissions significantly when compared to CMBD. Finally, the influence of the different feed compositions on VRC performance is also studied.

Determining the Number of Topics to Retain using Tools from Factor Analysis

Determining the optimal number of topics to retain in the conduct of topic modeling (TM) has received much attention over the last decade. Despite this work, issues remain regarding the best methods to use for making such determinations. Approaches involving the use of relatively simple statistics, most notably perplexity, have proven to be somewhat inconsistent. Recently, researchers have suggested the use of change in perplexity scores as a useful heuristic for determining the optimal number of topics to retain. The current study builds on this earlier work by assessing the utility of several methods borrowed from factor analysis and applied to statistics commonly used in topic modeling, including perplexity and Alpha. These new approaches are applied to several textual datasets and compared with more traditional methods for determining the number of topics to retain. Results of these analyses demonstrate that application of these methods borrowed from factor analysis does appear to be effective for identifying the number of topics to retain.

Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks

Due to the dynamic mobility feature, the proactive flow rule cache method has become one promising solution in software-defined networking (SDN)-based access networks to reduce the number of flow rule installation procedures between the forwarding nodes and SDN controller. However, since there is a flow rule cache limit for the forwarding node, an efficient flow rule cache strategy is required. To address this challenge, this paper proposes the mobility-aware hybrid flow rule cache scheme. Based on the comparison between the delay requirement of the incoming flow and the response delay of the controller, the proposed scheme decides to install the flow rule either proactively or reactively for the target candidate forwarding nodes. To find the optimal number of proactive flow rules considering the flow rule cache limits, an integer linear programming (ILP) problem is formulated and solved using the heuristic method. Extensive simulation results demonstrate that the proposed scheme outperforms the existing schemes in terms of the flow table utilization ratio, flow rule installation delay, and flow rules hit ratio under various settings.

Blockchain-IoT peer device storage optimization using an advanced time-variant multi-objective particle swarm optimization algorithm

The integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements. A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time-variant Multi-objective Particle Swarm Optimization Algorithm (AT-MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time-variant weights for the velocity of the particle swarm optimization and the non-dominated sorting and mutation schemes from NSGA-III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA-II), and the Pareto Envelope-based Selection Algorithm with region-based selection (PESA-II), and NSGA-III. The proposed AT-MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT-MOPSO achieved 52% energy efficiency compared to NSGA-III. To show how this algorithm can be applied to a real-world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT-MOPSO can be used with existing Blockchain systems and the benefits it provides.