Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intelligent Device Fault Diagnosis Method in Industrial Internet of Things

With the rapid development of various types of information technologies and sensor technologies, traditional industries are moving in the direction of the smart industry. The emergence of industrial Internet of Things can significantly increase manufacturing efficiency, improve product quality, and reduce product costs and resource consumption. However, the traditional signal processing based feature extraction plus classifier fault diagnosis method is no longer applicable to the characteristics of “big data” in the industrial Internet of Things. This article first proposes an intelligent diagnostic architecture for industrial IoT devices based on the edge-cloud architecture. Then based on the convolutional neural network, this paper proposes a method for fault diagnosis of equipment that is more suitable for industrial Internet of things. The method uses multi-channel to directly process the one-dimensional time series data collected by the sensor without data conversion, and the effectiveness and feasibility of the method are verified through comparative experiments.
Microwave Sensor for Liquid Dielectric Characterization Based on Metamaterial Complementary Split Ring Resonator

A metamaterial-based microwave sensor with complementary split ring resonator (CSRR) is implemented for dielectric characterization of liquids. The novelty in the proposed contactless sensor is the use of liquid samples placed normal to the sensor surface. Placed inside of glass capillary tubes, it is quickly possible to analyze the dielectric properties of liquids simply by replacing the capillary tubes with new samples. The liquid samples inside the glass capillary tubes modify the resonant frequency and <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factor of the CSRR sensor. Thereby, a relation between the sensor resonant frequency, <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factor, and complex permittivity of the liquid samples can be estimated. A measurement setup was used to test the proposed sensor that exhibited successfully detection of 10% steps in binary mixtures of ethanol and water. The proposed sensor is compact, low cost, contactless, reusable, easily fabricated, and easy to work.
A Flexible Retransmission Policy for Industrial Wireless Sensor Actuator Networks

Real-time and reliable communication is essential for industrial wireless sensor-actuator networks. To this end, researchers have proposed a wide range of transmission scheduling techniques. However, these methods usually employ a link-centric policy which allocates a fixed number of retransmissions for each link of a flow. The lack of flexibility of this approach is problematic because failures do not occur uniformly across links and link quality changes over time. In this paper, we propose a flow-centric policy to flexibly and dynamically reallocate retransmissions among the links of a multi-hop flow at runtime. This contribution is complemented by a method for determining the number of retransmissions necessary to achieve a user-specified reliability level under two failures models that capture the common wireless properties of industrial environments. We demonstrate the effectiveness of flow centric policies using empirical evaluations and trace-driven simulations. Testbed experiments indicate a flow-centric policy can provide higher reliability than a link-centric policy because of its flexibility. Trace-driven experiments compare link-centric and flow-centric policies under the two reliability models. Results indicate that when the two approaches are configured to achieve the same reliability level, a flow-centric approach increases the median real-time capacity by as much as 1.42 times and reduces the end-to-end response times by as much as 2.63 times.
Modeling and Mitigation of Rotor Eddy-Current Losses in High-Speed Solid-Rotor Induction Machines by a Virtual Permanent Magnet Harmonic Machine

Being much more rugged than a rotor made of traditional steel sheets, a solid rotor is frequently used in high-power high-speed induction machines (IMs). Furthermore, by a solid-rotor machine, a higher rotating speed can be achieved. However, the solid rotor also has certain disadvantages, the relatively high solid-rotor eddy-current losses being the most serious one. Basically, solid-rotor eddy-current losses are mainly induced by time–spatial air-gap flux density high-order harmonics. The suppression of the high-order harmonics is the key means to mitigate the rotor eddy-current losses. In this paper, a novel computational model is proposed for evaluating the rotor harmonic eddy-current losses of a 2 MW, 12 000 r/min IM. The model was built on a special machine referred to as a virtual permanent magnet harmonic machine (VPMHM). The model was constructed by applying the finite-element method (FEM), and it is based on the time–spatial harmonics produced by a rotating magnet with sinusoidal magnetization. The VPMHM model links the air-gap flux density harmonics directly to the specific rotor eddy-current losses. Furthermore, according to the proposed model, three options, namely, the use of air-gap permeance modifying notches on the stator teeth, semimagnetic wedges, and their combination, were investigated in detail for mitigating the rotor eddy-current losses in the motor nominal operating point. The results and conclusions in this paper are based on the FEM analysis.
Applying Recursive Temporal Blocking for Stencil Computations to Deeper Memory Hierarchy

Recent high performance computer architecture has deeper memory hierarchy including 3D stacking memory and non-volatile memory. In order to achieve higher application performance, optimizations in application algorithm level are required. This paper takes stencil computations as the target application, and focus on a technique called temporal blocking. In order to consider multiple hierarchy, we apply recursive temporal blocking alorithm. By using it, high performance is obtained without hand tuning considering memory architecture parameter. The evaluation of this approach is done on a server equipped with GPU device memory of HBM2, DDR4 host memory and 3D-XPoint based SSD.
PARC: A novel OS cache manager

To boost input‐output performance, operating systems employ a kernel‐managed caching space called the buffer cache or page cache. Given the limited size of a buffer cache, an effective cache manager is required to decide which blocks should be evicted from the cache. Previous cache managers use historical information to make replacement decisions. However, existing approaches are unable to maximize performance since they rely on limited historical information. Motivated by the limitations of existing solutions, this paper proposes a novel manager called the Pattern‐assisted Adaptive Recency Caching (PARC) manager. PARC simultaneously uses the historical information of recency, frequency, and access patterns to estimate the locality strengths of blocks and, upon a cache miss, evicts the block with the least strength. Specifically, PARC exploits the reference regularities exhibited in past input‐output behaviors to actively and rapidly adapt the recency and frequency information of blocks so as to precisely distinguish blocks with long‐ and short‐term utility. Through comprehensive simulations on a variety of traces of different access patterns, we show that PARC is robust since, except for random workloads where the performance of each cache manager is similar, PARC always outperforms existing solutions.
FCM Expert: Software Tool for Scenario Analysis and Pattern Classification Based on Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) have become a suitable and proven knowledge-based methodology for systems modeling and simulation. This technique is especially attractive when modeling systems characterized by ambiguity, and/or non-trivial causalities among its variables. The rich literature that is found related to FCMs reports very clearly many successful studies solved through the use of FCMs; however, when it comes to software implementations, where domain experts can design FCM-based systems, run simulations or perform more advanced experiments, not much is found or documented. The few existing implementations are not proficient in providing options for experimentation. Therefore, we believe that a gap exists, specifically between the theoretical advances and the development of accurate, transparent and sound FCM-based systems; and we advocate for the creation of more complete and exible software products. The goal of this paper is to introduce “FCM Expert”, a software tool for fuzzy cognitive modeling, where we focus on scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters that might define a model, optimize its network topology and improve the system convergence without losing information. Also, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.
Fully Nonvolatile and Low Power Full Adder Based on Spin Transfer Torque Magnetic Tunnel Junction With Spin-Hall Effect Assistance

As technology node scales down below 90 nm, the conventional complementary metal oxide semiconductor (CMOS) logic circuits suffer from various problems such as high standby power due to increase in leakage current. Spintronic devices based on magnetic tunnel junction (MTJ) is one of the most promising technology candidates for the future of the digital circuits. MTJ-based logic circuits can offer nonvolatility, high endurance, high density, low standby power dissipation, and 3-D integration capability with the CMOS technology. In recent years, several full-adder circuits are proposed based on MTJs that are not fully nonvolatile since they use MTJs to store only one of their inputs. This paper proposes a fully nonvolatile magnetic full-adder (MFA) circuit offering the capability of power gating with no loss of data. The proposed circuit stores all the inputs in MTJs using the spin-transfer torque (STT) method assisted by the spin-Hall effect (SHE). Thanks to the SHE assistance, delay and power consumption of the MTJ switching are reduced significantly. Moreover, as a result of lower write current, the endurance of the oxide barrier can be increased. Using a three-terminal SHE–STT–MTJ model and a 45 nm CMOS SPICE model, we simulated and validated the functionality of our design and compared it with some recent previous MFAs. Simulation results reveal that the proposed MFA offers considerable superiorities over the recent counterparts.
The Dropout Method of Face Recognition Using a Deep Convolution Neural Network

With the rapid development of artificial intelligence and pattern recognition, face recognition has become a hot topic in the field of computer vision. Especially after the deep learning proposed, the performance of face recognition algorithm has been greatly improved. This paper mainly introduces the main method of face recognition using a deep convolution neural network model. First, for training network parameters, we use a fast convergence stochastic gradient algorithm (SGD). At the same time, the "dropout" method is added to each layer of the network to hide some neuron activity by a certain probability, in order to avoid the over fitting problem caused by the deep network model. Through the above process, a neural network model can recognize face images.
Ventricular global longitudinal strain is altered in children with sickle cell disease

Cardiac disease is the primary cause of death in sickle cell disease (SCD). Right and left ventricular global longitudinal strain (RVGLS, LVGLS) are early markers of systolic dysfunction but are not well investigated among children with SCD. One hundred and forty‐three patients with HbSS or HbSβ0‐thalassaemia (median age 11 years, range 5–19 years) and 71 controls matched for age and sex were compared. RVGLS and LVGLS were measured and compared with conventional measures of echocardiography and markers of haemolysis and inflammation. RVGLS was higher in children with SCD than in controls (−25·72% ± 3·45% vs. −24·54% ± 2·41%, P = 0·005); LVGLS was not different. RVGLS decreased with older age in children with SCD (ρ = 0·338, P < 0·001) but not among controls. Decreased RVGLS was associated with increased left atrial end diastolic volume (ρ = 0·181, P = 0·04); RVGLS increased with cardiac output (r = −0·279, P = 0·01). RVGLS and LVGLS were not associated with disease‐modifying therapies, degree of anaemia or haemolysis markers. Elevated RVGLS may indicate an early RV compensatory mechanism in response to upstream myocardial insults and elevated cardiac output. Global longitudinal strain may serve as an early marker of altered myocardial function in children with SCD.
Real-world treatment patterns, resource use and costs of treating uncontrolled carcinoid syndrome and carcinoid heart disease: a retrospective Swedish study

Abstract Objectives: To quantify healthcare resource use (HRU) and costs in relation to carcinoid syndrome (CS) and carcinoid heart disease (CHD) in a real-world setting, and to provide perspective on treatment patterns. Materials and methods: Patient data and HRU were collected retrospectively from three Swedish healthcare registers. Adult patients diagnosed with metastatic gastroenteropancreatic neuroendocrine tumors (GEP-NETs) grade 1 or 2 and CS who purchased somatostatin analogs (SSAs), and experienced controlled (defined by SSAs use) and uncontrolled (defined by SSAs dose escalation) CS for ≥8 months during the study period were included. Patients diagnosed with CHD from the date of the GEP-NET diagnosis were included in the CHD study group. Results: Overall, total HRU cost increased with uncontrolled CS and CHD. Total resource cost was 15,500€/patient during controlled CS (8 months), rising to 21,700€/patient during uncontrolled CS (8 months), representing an increase of ∼40% (6200€/patient). Costs/patient were driven mainly by SSA use, tumor-related medical interventions and examinations. The total mean cost/year of disease was 1100€/patient without CHD, compared to 4600€/patient with CHD, a difference of 3500€/patient. Excluding SSA cost burden, the main drivers of increased cost in CHD patients were surgical interventions and echocardiography. Conclusions: This study provides a comprehensive overview of the treatment patterns and burden of uncontrolled CS symptoms and CHD using Swedish national register data. Increases in medical interventions and examinations HRU and increased SSA use suggest that SSA dose escalation alone may not effectively control the symptoms associated with uncontrolled CS, highlighting an unmet treatment need in this patient group.
The Effects of Mg Contents on MgxZn1−xO-Based Metal-Semiconductor-Metal Ultraviolet Photodetectors by Ultrasonic Spray Pyrolysis Deposition

The Mg<sub>x</sub>Zn<sub>1−x</sub>O thin films with Mg/Zn mole fraction of 0.1/0.9, 0.2/0.8, and 0.3/0.7 were deposited by the ultrasonic spray pyrolysis deposition method. The X-ray diffraction, X-ray photoelectron spectroscopy, and Raman were used to analyze the material characteristics, including crystal structures, crystal orientations, chemical qualitative, and quantitative characteristics. The photoluminescence spectrum was used to observe the near-band-edge emission and trap energy level. The refractive index and extinction coefficient of the Mg<sub>x</sub>Zn<sub>1−x</sub>O thin films were measured by the ellipsometer. The metal-semiconductor-metal structure photodetectors were fabricated. The electrical characteristics, including current-voltage and Schottky barrier height characteristics were investigated. The photoresponse showed that the Mg<sub>0.1</sub>Zn<sub>0.9</sub>O-based photodetector had the highest photoresponsivity and the Mg<sub>0.3</sub> Zn<sub>0.7</sub>O-based photodetector had the shortest response time and the highest detectivity. Further, the contents of Mg in the Mg<sub>x</sub>Zn<sub>1−x</sub>O affected the cutoff wavelength of the photodetector.
Adaptive Genetic Algorithm for Improving Prediction Accuracy of a Multi-Criteria Recommender System

Recommender systems are software tools used to make valuable recommendations to users. Traditionally, recommender systems use information obtained from ratings of an item by users with similar opinions to make recommendations. A user uses a single rating to represent the degree of likeness of an item in traditional recommender systems. Though this approach has reasonably shown a good prediction accuracy, however, the performance of traditional recommender systems is considered inadequate, as users could have different opinions based on some specific features of an item. Multi-criteria recommendation extends the traditional techniques by incorporating ratings for various attributes of the items. It provides better recommendations for users as the system allows the opportunity for users to specify their preferences based on different attributes of user item, which improves prediction accuracy. In this paper, we proposed an aggregation function based method that uses an adaptive genetic algorithm to efficiently incorporate the criteria ratings for improving the accuracy of the multi-criteria recommender system. We carried out an experiment using a dataset for multi-criteria recommendations of movies to users. The experimental result shows that our proposed approach provides better accuracy than the corresponding traditional technique.
Chemotypic Variation of Conocephalum salebrosum in the Southeastern Appalachian Range: A Search for Cryptic Plant Biodiversity Around the Tennessee River Valley

The chemotaxonomy of the Conocephalum spp. complex, based on GC-MS analysis of the volatile compositions, has helped to reveal cryptic biodiversity and delineate actual distribution patterns of chemotypes. In the Appalachian Mountains, two samples from eastern central part of the range were previously shown to be C. salebrosum. Additionally, it has recently come to light that stress can alter the volatile composition of C. conicum. This study address a previously unsampled region of the southeastern Appalachians, a region that is a biodiversity epicenter, to determine if more chemotypic diversity remains to be seen for the Conocephalum spp. complex. A common garden experiment was performed, but yielded more of a common stress experiment, and significantly altered the volatile compositions. Wild-collected controls and a meta-analysis of these data and those from previous works suggest that the common garden experiment caused stress and that the liverworts sampled belong to the C. salebrosum clade of of the Conocephalum spp. complex.
Analysis for Axial Force of Slotted-Type Axial-Flux Magnetic Coupler

The mechanical properties have an important effect on the stability and optimization of magnetic coupler. Therefore, axial force as part of mechanical properties is necessary to be explored in-depth. In order to calculate the axial force of slotted-type axial-flux magnetic coupler (SAM C), the magnetic flux density is derived by using the equivalent magnetic circuit method. Based on that, the formulas for the axial force are obtained by utilizing the equivalent magnetic charge method. The axial forces under different angular displacements are analyzed by using the discrimination method, then the influence of number of poles and air-gap lengths on the maximum axial force are studied. The results derived from analytical model are compared with those obtained from finite elements simulations. Calculations and simulations show that the maximum axial force appears at angular displacement is 0°, and the maximum value rises sharply when the air-gap length is below 3 mm. As the number of poles increases, the maximum axial force decreases gradually.
Insect Pheromone Receptors – Key Elements in Sensing Intraspecific Chemical Signals

Pheromones are chemicals that serve intraspecific communication. In animals, the ability to detect and discriminate pheromones in a complex chemical environment substantially contributes to the survival of the species. Insects widely use pheromones to attract mating partners, to alarm conspecifics or to mark paths to rich food sources. The various functional roles of pheromones for insects are reflected by the chemical diversity of pheromonal compounds. The precise detection of the relevant intraspecific signals is accomplished by specialized chemosensory neurons housed in hair-like sensilla located on the surface of body appendages. Current data indicate that the extraordinary sensitivity and selectivity of the pheromone-responsive neurons (PRNs) is largely based on specific pheromone receptors (PRs) residing in their ciliary membrane. Besides these key elements, proper ligand-induced responses of PR-expressing neurons appear to generally require a putative co-receptor, the so-called “sensory neuron membrane protein 1” (SNMP1). Regarding the PR-mediated chemo-electrical signal transduction processes in insect PRNs, ionotropic as well as metabotropic mechanisms may be involved. In this review, we summarize and discuss current knowledge on the peripheral detection of pheromones in the olfactory system of insects with a focus on PRs and their specific role in the recognition and transduction of volatile intraspecific chemical signals.
Factors Associated With Older Adults’ Enrollment in Balance Classes to Prevent Falls: Case-Control Study

Balance training decreases fall risk among older adults, but few participate in such training. We examined the association of exposure to social marketing to promote balance classes, personal characteristics and other factors, with older adults’ balance class participation. Adults aged ⩾60 years were eligible for this case-control study if they attended any church enrolled in a trial testing the effect of social marketing on balance class participation. Cases attended balance classes during the study period; controls were randomly sampled congregants who did not join a class. Cases were more likely to attend churches that received the social marketing program, and were older, more often female, and more frequently experienced “near falls” than controls. Participation was also associated with increasing age of the church’s leader and rural church attendance. Programs to promote balance classes may need to be tailored to target some risk groups, including men and urban and suburban congregants.
A Novel Dual-platform Microwave Staring Correlated Imaging Method in Cooperative-mode

In order to realize remote sensing monitoring in a large range as well as high-resolution imaging of important targets in a small part, a new dual-platform microwave staring correlated imaging (MSCI) scheme in cooperative-mode is proposed, which includes two MSCI processes. The first is only based on geostationary satellite platform with randomly radiating and synchronously receiving, which can achieve the low-resolution monitoring in a wide range to discover the important targets; The second is the collaboration based on both the geostationary satellite platform with randomly radiating and the aerostat platform with synchronously receiving in a wide space span, which can obtain the high-resolution imaging of the important targets in a small scene. Numerical simulations are performed to validate its effectiveness of the proposed scheme.