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Copyright of Artnodes is the property of Universitat Oberta de Catalunya 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.)

Copyright of Journal of Community Service & Empowerment is the property of Journal of Community Service & Empowerment 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.)

A digital-enabled safety management approach is increasingly crucial for crane operations, which are common yet highly hazardous activities sensitive to environmental dynamics on construction sites. However, there exists a knowledge gap regarding the current status and developmental trajectory of this approach. Therefore, this paper aims to provide a comprehensive overview of digital technologies for enhancing crane safety, drawing insights from articles published between 2008 and 2021. Special emphasis is placed on the sensing devices currently in use for gathering "man-machine-environment" data, as well as the communication networks, data processing algorithms, and intuitive visualization platforms employed. Through qualitative and quantitative analysis of the literature, it is evident that while notable advancements have been made in digital-enabled crane safety management, these achievements remain largely confined to the experimentation stage. Consequently, a framework is proposed in this study to facilitate the practical implementation of digital-enabled crane safety management. Furthermore, recommendations for future research directions are presented. This comprehensive review offers valuable guidance for ensuring safe crane operations in the construction industry. [ABSTRACT FROM AUTHOR]

Investing in the stock market is one of the world's best ways to generate wealth. This study uses the R programming language and statistical methods to analyze and visualize a dataset publicly available on Kaggle that includes >3,800 stocks, their associated financial indicators, and the sector that classifies that stock in the macro-area. After data preprocessing, we evaluated 95% confidence intervals and a chi-squared test to determine that the financial services sector had the most significant proportion of profitable stocks and, on average, was the most net profitable sector in magnitude. This study then transitions from profitability to technical indicators and establishes a moderate positive correlation between the price-to-earnings ratio and payout ratio. Finally, this study analyzes these financial indicators on a per-sector basis using probability density functions. These inferences allow mathematicians and investors alike to analyze and make appropriate risk-optimization conclusions when diversifying stock portfolios by sector. [ABSTRACT FROM AUTHOR]

Academic information systems reflect the quality of management of a college and are one of the efforts of educational institutions to provide quality services to provide convenience and comfort to students. There are obstacles such as the difficulty of controlling the scheduling of courses and information that is less updated on academic information systems. This study aims to determine how much influence the academic information system has on student satisfaction. This study uses a type of research based on a quantitative approach. The research subjects were bachelor and graduate school students from Universitas Negeri Yogyakarta. The data collection method used a questionnaire. Data processing techniques and data analysis using simple linear regression formulas. The results showed that (i) there was a significant influence between the implementation of academic information systems on the level of satisfaction of Universitas Negeri Yogyakarta bachelor and graduate school students amount 41% (ii) supporting and inhibiting factors perceived by students in the implementation of academic information systems found in speed, timeliness, and ease of access. This article has empirical contributions related to academic information systems in higher education and can be used as insights for policy holders in universities in making policies. [ABSTRACT FROM AUTHOR]

The Connectivity plays a vital role in today's world when it comes to a device or things connection. The Internet of Things (IoT) plays a fundamental role in understanding real-world objects virtually and generating massive amounts of data from and for them. The exponential rise in data consumption and the exploding number of IoT-connected devices are only two indicators of how the expansion of Big-Data (BD) exactly parallels that of the IoT. Concerns of data collecting efficiency, data processing, analytics, and security are not trivial when it comes to managing big data in a constantly growing network. Researchers have looked at the difficulties of implementing the Internet of Things to find solutions to these issues. Despite the abundance of research on big data, analytics, and the Internet of Things, the intersection of these fields opens several prospects for developing big data and analytics in IoT settings. This paper focuses on feasible and ideal solutions for the effective integration of IoT with BD. The challenges of combining IoT and BD were also illustrated by presenting an analysis of this issue to a community of readers. [ABSTRACT FROM AUTHOR]

Animal models have long been used to study gene function and the impact of genetic mutations on phenotype. Through the research efforts of thousands of research groups, systematic curation of published literature and high- throughput phenotyping screens, the collective body of knowledge for the mouse now covers the majority of protein-coding genes. We here collected data for over 53 000 mouse models with mutations in over 15 000 genomic markers and characterized by more than 254 000 annotations using more than 9000 distinct ontology terms. We investigated dimensional reduction and embedding techniques as means to facilitate access to this diverse and high-dimensional information. Our analyses provide the first visual maps of the landscape of mouse phenotypic diversity. We also summarize some of the difficulties in producing and interpreting embeddings of sparse phenotypic data. In particular, we show that data preprocessing, filtering and encoding have as much impact on the final embeddings as the process of dimensional reduction. Nonetheless, techniques developed in the context of dimensional reduction create opportunities for explorative analysis of this large pool of public data, including for searching for mouse models suited to study human diseases. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has shown outstanding performance and potential in pattern recognition and feature extraction, which has attracted an increasing amount of attention from engineering researchers and academics. Fault diagnosis methods based on deep learning have also become the focus of a significant amount of research. In this paper, a nonlinear process fault diagnosis and identification method based on DBN‐dropout is proposed. The deep belief network (DBN) has significant advantages in dealing with nonlinear processes, and it can extract the abstract representation of nonlinear process data to build a deep network to achieve the real‐time monitoring of process operations. Dropout technology can reduce overfitting and improve the generalization ability of the model. Afterwards, the Tennessee Eastman (TE) process is employed to analyze the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Unlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In this paper, a process fault diagnosis method using multi‐time scale dynamic feature extraction based on convolutional neural network (CNN) consisting of similarity measurement, variable ranking, and multi‐time scale dynamic feature extraction is proposed. The CNN‐based model containing the fixed multiple sampling (FMS) layer can extract dynamic characteristics of process data at different time scales. The benchmark Tennessee Eastman (TE) process is used to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) and the seismic surface wave method are two geophysical techniques commonly used in near-surface surveys up to a depth of tens of meters. GPR can be utilized to clearly distinguish lithologic interfaces, while the seismic surface wave method (hereby referred to as the Rayleigh wave) is employed for its high sensitivity to the shear (S) wave velocity. However, the propagation velocity of the electromagnetic (EM) wave which is both initiated and tracked by GPR is difficult to determine within the stratum. Unfortunately, this can affect the time–depth conversion and interpretation of the radargram. Moreover, both the horizontal resolution and the detectability of the properties of the shallow stratum are limited by the seismic geometry of the Rayleigh wave data. It is important to note that the non-uniqueness of geophysical inversion problem also generates additional biases. To overcome the problems mentioned above, we put forward an interactive integrated geophysical system that utilizes the thickness as a bridge to connect GPR and the Rayleigh wave data in the data processing and inversion. In this study, we employed the velocity of a radagram and the Rayleigh wave dispersion curve inversion based on a genetic algorithm to reconstruct the near-surface distribution. Then, we set the thickness derived from the GPR data as limit of the Rayleigh wave dispersion curve inversion. Hence, we applied the constrained inversion result to determine the most accurate EM wave velocity. As expected, both the constrained inversion of the Rayleigh wave dispersion curve and the velocity correction of the radargram performed better than did the geophysical method alone, both in the numerical models and in the field. Finally, we found the interactive integrated geophysical system to be more conducive for geological interpretation in near-surface surveys. [ABSTRACT FROM AUTHOR]

This article continues our discussion on database programming by exploring methods of downloading data from web sites, loading them on databases, and analyzing them. [ABSTRACT FROM AUTHOR]

In recent years, Linear Non-Gaussian Acyclic Model (LiNGAM) has been widely used for the discovery of causal network. However, solutions based on LiNGAM usually yield high computational complexity as well as unsatisfied accuracy when the data is high-dimensional or the sample size is too small. Such complexity or accuracy problems here are often originated from their prior selection of root nodes when estimating a causal ordering. Thus, a causal discovery algorithm termed as GPL algorithm (the LiNGAM algorithm of Giving Priority to Leaf-nodes) under a mild assumption is proposed in this paper. It assigns priority to leaf nodes other than root nodes. Since leaf nodes do not affect others in a structure, we can directly estimate a causal ordering in a bottom-up way without performing additional operations like data updating process. Corresponding proofs for both feasibility and superiority are offered based on the properties of leaf nodes. Aside from theoretical analyses, practical experiments are conducted on both synthetic and real-world data, which confirm that GPL algorithm outperforms the other two state-of-the-art algorithms in computational complexity and accuracy, especially when dealing with high-dimensional data (up to 200) or small sample size (down to 100 for the dimension of 70). [ABSTRACT FROM AUTHOR]

The existing outlier detection algorithm does not process the original data, resulting in too high computational time complexity and unsatisfactory detection results. This paper proposed a two-stage outlier detection algorithm NLOF based on grid filtering. First it used grid filtering to screen the original data initially, put data with a density less than a certain threshold into a candidate exception subset; then in order to further optimize the density-based algorithm, based on the k-neighborhood, according to the ratio of the number of data points in the neighborhood to the area of the circle formed by the neighborhood, as the basis for calculating the data point density, outlier detection obtained a more accurate outlier set. Experiments was carried out on a variety of public datasets. Experiments show that this method can achieve good performance in anomaly detection and reduce the time complexity of the algorithm. [ABSTRACT FROM AUTHOR]

Compared with traditional neural networks, extreme learning machine (ELM) shows outstanding performances on speed and computation. Aiming at the problems that ELM needs more hidden layer neurons and meaningful features of data sometimes are sacrificed in order to improve the training speed, a novelty network multi-parallel extreme learning machine with excitatory and inhibitory neurons (MEI-ELM) is proposed based on the idea of biological neurons. In MEI-ELM, (1) A parallel system is introduced to make it more compact and reduce the number of hidden layer neurons. (2) The property of excitatory and inhibitory of biological neuronal for data processing is introduced to improve its performance. Through applying MEI-ELM, ELM, Fast Learning Network (FLN) and Fast Learning Network with Parallel Layer Perceptrons (PLP-FLN) to 11 classical regression problems, it can be obtained that MEI-ELM performs much better than the other methods in generalization and stability. [ABSTRACT FROM AUTHOR]

Anchoring temporary shelters is a repetitive and time-consuming task mostly accomplished by hand or handheld tools. Therefore, such anchors should be lightweight and easy to install. Experimental investigations on the effect of anchor geometry on ease of installation and uplift capacity of a double-helix anchor are presented. The double-helix anchor with 1.78 kN uplift capacity was tested on various soil types and conditions. Similar tests were carried out using the same anchors except that the helices were perforated for the first test and notched for the second test. The experimental setup was arranged to allow measurement of installation torque, installation time, and pullout load. It was observed that the uplift capacity and weight of the anchor dropped with the reduction in the surface area of the helices. The drop in the uplift capacities of the anchor with perforated helices and that with perforated and notched helices were 1.05 kN and 1.2 kN, respectively. Weight loss of 0.45 kg due to perforation and 0.88 kg due to notching were recorded. The minimum uplift capacity of the anchor with both perforated and notched helix was 1.84 kN. The ease of installation of the anchors was assessed based on the anchor driving torque and installation time. A general trend of reduction in the magnitude of anchor driving torque and installation time with the reduction in helix surface area was observed. Thus, perforating and notching helix surfaces could reduce the anchor weight and improve drivability. [ABSTRACT FROM AUTHOR]

The article describes the developed algorithm for the formation of the training load, covering the whole cycle of operations, including: obtaining the initial data and its processing, aggregating the disciplines, calculating the hours, forming a list of teachers, and downloading the information as the form of a spreadsheet. [ABSTRACT FROM AUTHOR]

A connected component in a graph is a set of nodes linked to each other by paths. The problem of finding connected components has been applied to diverse graph analysis tasks such as graph partitioning, graph compression, and pattern recognition. Several distributed algorithms have been proposed to find connected components in enormous graphs. Ironically, the distributed algorithms do not scale enough due to unnecessary data IO & processing, massive intermediate data, numerous rounds of computations, and load balancing issues. In this paper, we propose a fast and scalable distributed algorithm PACC (Partition-Aware Connected Components) for connected component computation based on three key techniques: two-step processing of partitioning & computation, edge filtering, and sketching. PACC considerably shrinks the size of intermediate data, the size of input graph, and the number of rounds without suffering from load balancing issues. PACC performs 2.9 to 10.7 times faster on real-world graphs compared to the state-of-the-art MapReduce and Spark algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep learning method with convolutional neural networks (CNNs) using skip connections with layer groups for super-resolution image reconstruction. In the proposed method, entire CNN layers for residual data processing are divided into several layer groups, and skip connections with different multiplication factors are applied from input data to these layer groups. With the proposed method, the processed data in hidden layer units tend to be distributed in a wider range. Consequently, the feature information from input data is transmitted to the output more robustly. Experimental results show that the proposed method yields a higher peak signal-to-noise ratio and better subjective quality than existing methods for super-resolution image reconstruction. [ABSTRACT FROM AUTHOR]

Abstract—We present the results of magnetovariational soundings at two sites (Tiksi Observatory and Kotelny Island in the Laptev Sea region of the Arctic) and their three-dimensional (3D) inversion using the ModEM program. In the models obtained by the inversion, the conductive heterogeneities are present in the regions of the both sites down to a depth of 200 km in the region of the observatory and 100 km beneath the Kotelny Island. The geoelectric heterogeneities in the model in the region of the observatory are most contrasting and voluminous, whereas beneath the island they are more localized. The correlation between the locations of these heterogeneities at both sites and the features of the geological and geophysical structure of the region is noted. It is shown that the applied algorithm of data processing eliminates the effect of the polar electrojet which provides the possibility to study the geoelectric structure of the region by magnetovariational method. [ABSTRACT FROM AUTHOR]

Understanding how developers interact with different software artifacts when performing comprehension tasks has a potential to improve developers' productivity. In this paper, we propose a method to analyze eye-tracking data using process mining to find distinct reading patterns of how developers interacted with the different artifacts. To validate our approach, we conducted an exploratory study using eye-tracking involving 11 participants. We applied our method to investigate how developers interact with different artifacts during domain and code understanding tasks. To contextualize the reading patterns and to better understand the perceived benefits and challenges participants associated with the different artifacts and their choice of reading patterns, we complemented the eye-tracking data with the data obtained from think aloud. The study used behavior-driven development, a development practice that is increasingly used in Agile software development contexts, as a setting. The study shows that our method can be used to explore developers' behavior at an aggregated level and identify behavioral patterns at varying levels of granularity. [ABSTRACT FROM AUTHOR]

Object recognition near coastal area is the hotspot in recent years. However, the problems such as lack of precision, low efficiency and low fault tolerance of these traditional methods are an obstacle of hyperspectral image. This paper proposes an improved method on U-net deep learning model to identify these categories. Firstly, data enhancement processing is implemented on mini-batch×mini-batch×3 patches of cropped randomly images to improve the generalization ability of training network. Secondly, an improved U-net deep learning model is developed to extract the feature information automatically. Next, the improved model can be trained repeatedly by the cross-entropy loss function and INadam optimization algorithm to seek better results. Finally, obtained optimal model is used to extract the object information accurately. Simulation and experimental results show that the proposed method has better detection performance, higher training accuracy with lower loss fluctuation than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have made remarkable success in image classification. However, it is still an open problem how to develop new models instead of CNNs. Here, we propose a novel model, namely two-dimensional perceptron (TDP), to get direct input of 2D data for further processing. A TDP computes hidden neurons from the input via left/right matrix multiplication, producing left-weighted TDP and right-weighted TDP, respectively. Experimental results on MNIST and COIL-20 datasets show that, in cases with the same number of hidden neurons, the model obtains 5%–45% relative performance improvement and 2 ×–36× speedup in comparison with the corresponding multilayer perceptron and convolutional neural network. Hence, it is a promising and potential model that may open some new directions for deep neural networks, particularly alternatives to CNNs. [ABSTRACT FROM AUTHOR]

The squinted spotlight SAR(Synthetic Aperture Radar) is an effective imaging mode for obtaining high-resolution SAR imagery in advance. However, a large-range cell migration heavily burdens the storage equipment, and the SAR system should consider its high risk for blind range and Nadir interference. Recently, the PRF(Pulse Repetition Frequency) variation and ASRW(Adaptive Sliding Range Window) have been proposed as effective techniques for squinted SAR image acquisition. This paper presents a signal processing algorithm for the squinted variable PRF spotlight-SAR. The algorithm is a two-step focusing algorithm combined with the interpolation method for compensating distortions due to PRF variation. The simulated variable PRF SAR raw data and processing results demonstrate the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

New innovative methods, such as augmented reality, are becoming more and more popular through the possibility of implementation the virtual elements into real-world environments. The aim of the present contribution is to link the techniques used in the educational process with the needs of real practice. The contribution points to the possibilities of usage not only of CL data for machining process, but shows possibilities comprising the use simulation data from the pre-production phase for augmented reality. Consequently a direct visualization solution for the operator is proposed, as well as an enriched information graphics environment for students to track the production process in real time. [ABSTRACT FROM AUTHOR]

• Maneuvering movements cannot be timely modeled by pre-defined multiple models. • A DeepMTT network is proposed to learn the maneuvering movements before tracking. • An LAST database is built to offer maneuvering movement data for network training. • The well-trained DeepMTT network is validated by abundant tracking simulations. In the field of radar data processing, traditional maneuvering target-tracking algorithms assume that target movements can be modeled by pre-defined multiple mathematical models. However, the changeable and uncertain maneuvering movements cannot be timely and precisely modeled because it is difficult to obtain sufficient information to pre-define multiple models before tracking. To solve this problem, we propose a deep learning maneuvering target-tracking (DeepMTT) algorithm based on a DeepMTT network, which can quickly track maneuvering targets once it has been well trained by abundant off-line trajectory data from existent maneuvering targets. To this end, we first build a LArge-Scale Trajectory (LAST) database to offer abundant off-line trajectory data for network training. Second, the DeepMTT algorithm is developed to track the maneuvering targets using a DeepMTT network, which consists of three bidirectional long short-term memory layers, a filtering layer, a maxout layer and a linear output layer. The simulation results verify that our DeepMTT algorithm outperforms other state-of-the-art maneuvering target-tracking algorithms. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications have massive client connections to cloud servers, and the number of networked IoT devices is remarkably increasing. IoT services require both low-tail latency and high concurrency in datacenters. This study aims to determine whether an order of magnitude improvement is possible in tail latency and concurrency in mainstream systems by proposing a hardware–software codesigned labeled network stack (LNS) for future datacenters. The key innovation is a cross-layered payload labeling mechanism that distinguishes different requests by payload across the full network stack, including application, TCP/IP, and Ethernet layers. This type of design enables prioritized data packet processing and forwarding along the full datapath, such that latency-insensitive requests cannot significantly interfere with high-priority requests. We build a prototype datacenter server to evaluate the LNS design against a commercial X86 server and the mTCP research, using a cloud-supported IoT application scenario. Experimental results show that the LNS design can provide an order of magnitude improvement in tail latency and concurrency. A single datacenter server node can support over 2 million concurrent long-living connections for IoT devices as a 99-percentile tail latency of 50 ms is maintained. In addition, the hardware–software codesign approach remarkably reduces the labeling and prioritization overhead and constrains the interference of high-priority requests to low-priority requests. [ABSTRACT FROM AUTHOR]

Copyright of Economía, Sociedad y Territorio is the property of El Colegio Mexiquense 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 presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces. [ABSTRACT FROM AUTHOR]

Processing programs as data is one of the successes of functional and logic programming. Higher-order functions, as program-processing programs are called in functional programming, and meta-programs, as they are called in logic programming, are widespread declarative programming techniques. In logic programming, there is a gap between the meta-programming practice and its theory: The formalizations of meta-programming do not explicitly address its impredicativity and are not fully adequate. This article aims at overcoming this unsatisfactory situation by discussing the relevance of impredicativity to meta-programming, by revisiting former formalizations of meta-programming, and by defining Reflective Predicate Logic, a conservative extension of first-order logic, which provides a simple formalization of meta-programming. [ABSTRACT FROM AUTHOR]

High speed data streams are everywhere in the fifth generation (5G) system measurement. To develop a high efficient and low cost 5G testbed in a reverberation chamber, the authors propose a high efficient measurement method in the time, frequency and space coherence measurement. In this study, the one-bit technique is introduced to measure the time domain, frequency domain and space domain correlations in a reverberation chamber. The coherence time, coherence bandwidth and correlated angle can all be measured using the one-bit correlation; float number calculations are simplified to logical operations which accelerate the data processing greatly. Measurements are performed to verify the results. [ABSTRACT FROM AUTHOR]

A novel method for detecting use-after-free bugs based on the program dynamic analysis is described. In memory unsafe programming languages, such as C or C++, this class of bugs mainly occurs when the program tries to access an area of dynamically allocated memory that has been already freed. For each program execution path, the method checks the correction of the allocation, deallocation, and access operations. Since the dynamic analysis is used, bugs can be found only in the parts of the code that was actually executed. The symbolic program execution with the help of SMT (Satisfiability Modulo Theories) solvers is used. This allows us to generate data the processing of which produces new execution paths. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANN) are important data processing algorithms which are used for solving nonlinear problems. Through classical approaches, mathematical infrastructure and complex equations in ANN are difficult to understand. Interactive and multimedia-based courseware has the potential to overcome these difficulties. In this study, a web based educational courseware for ANN was developed to provide an effective and efficient learning environment so that the difficulties can be overcome. This interactive courseware was also enriched with animations and text-based course contents. In addition to this, the effects of ANN parameters' changes were observed directly through graphical results. In this way, users can easily understand the fundamentals and working mechanism of ANN. Without using any commercial libraries, the courseware was developed with ASP.NET, an object-oriented programming language. The courseware supports file formats such as XML, TXT, and CSV so that it can co-operate with other software. "Balance and Scale" data set was used to evaluate the performance of the courseware. 0.9918 accuracy, 1 specificity and 1 sensitivity values were achieved. When this study is compared to previous studies, improvements in terms of visuality, understandability and interactivity can clearly be identified. [ABSTRACT FROM AUTHOR]

Functional dependencies (FDs) play a very important role in many data management tasks such as schema normalization, data cleaning, and query optimization. Meanwhile, there are ever-increasing application demands for efficient FD discovery on large-scale datasets. Unfortunately, due to huge runtime and memory overhead, the existing single-machine FD discovery algorithms are inefficient for large-scale datasets. Recently, distributed data-parallel computing has become the de facto standard for large-scale data processing. However, it is challenging to design an efficient distributed FD discovery algorithm. In this paper, we present SmartFD, which is an efficient and scalable algorithm for distributed FD discovery. First, we propose a novel attribute sorting-based algorithm framework. Next, to discover all the FDs grouped by a given attribute, we propose an efficient distributed algorithm Attribute-centric Functional Dependency Discovery (AFDD). In AFDD, we design an Fast Sampling and Early Aggregation (FSEA) mechanism to improve the efficiency of distributed sampling and propose a memory-efficient index-based method for distributed FD validation. Moreover, AFDD employs an attribute-parallel method to accelerate the pruning-and-generation of candidate FDs. Furthermore, we propose an adaptive switching strategy between distributed sampling and distributed validation based on the unified time-based efficiency metric. Also, we employ a distributed probing based method to make the switching strategy more accurate. Experimental results on Apache Spark reveal that SmartFD outperforms the state-of-the-art single-machine algorithm HyFD and the existing distributed algorithm HFDD with 3.2×–44.9× and 2.5×–455.7× speedup respectively. Moreover, SmartFD achieves good row scalability and column scalability. Additionally, SmartFD has sub-linear node scalability. [ABSTRACT FROM AUTHOR]

This paper presents processing-node status-based message scattering and gathering algorithms for multi-processor systems on chip to reduce the communication time between processors. In the message-scattering part of the message-passing interface (MPI) scatter function, data transmissions are ordered according to the proposed linear algorithm, based on the processor status. The MPI hardware unit in the root processing node checks whether each processing node's status is 'free' or 'busy' when an MPI scatter message is received. Then, it first transfers the data to a 'free' processing node, thereby reducing the scattering completion time. In the message-gathering part of the MPI gather function, the data transmissions are ordered according to the proposed linear algorithm, and the gathering is performed. The root node receives data from the processing node that wants to transfer first, and reduces the completion time during the gathering. The experimental results show that the performance of the proposed algorithm increases at a greater rate as the number of processing nodes increases. [ABSTRACT FROM AUTHOR]

With the growing complexity of industrial processes, the scale of production processes tends to be large. The significant amount of measurement data in large‐scale processes poses challenges in data collection, management, and storage. In order to perform effective process monitoring in large‐scale processes, the distributed process monitoring strategy is widely applied. Meanwhile, product quality is an important indicator for industrial production. Therefore, a novel quality‐based distributed process monitoring scheme is proposed. Firstly, the Girvan‐Newman (GN) algorithm in complex network divides process variables into multiple sub‐blocks. Secondly, greedy algorithm‐based high‐dimensional mutual information (HDMI) is used to extract quality‐related variables in each sub‐block, through which the irrelevant and redundant variables are eliminated. Thirdly, the decomposed modified partial least squares (DMPLS) approach is used to detect whether a fault is quality‐related or not in each sub‐block. Finally, the Bayesian inference strategy is adopted to combine the detection results of all sub‐blocks. The effectiveness of the distributed DMPLS approach is illustrated through a numerical simulation and the Tennessee Eastman (TE) process. The results show the superiority of our proposed monitoring scheme. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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 hip fracture is one of the most common diseases for elder people and also, one of the most worrying one since it usually is the starting point of further complications for both, the health of the patient and their daily life. Additionally, reports shown that there exist differences between people living in different regions, thus limiting the use of global models. In this work we propose a hip fracture prediction tool for a local region, using clinical data of the population of that region. The data is processed with a dimensionality reduction tool in combination with and hyper-parametrization process and the corresponding hyper-parameter optimization process for obtaining good predictions in the diagnoses, as the results shown. [ABSTRACT FROM AUTHOR]