Miracle-dz/UniFAR-FTUs huggingface dataset:
Diurnal cycle and dipolar pattern of precipitation over Borneo during an MJO event: Lee convergence and offshore-propagation

Surface precipitation anomalies over Maritime Continent islands typically lead oceanic precipitation by a week in the form of dipolar pattern before the arrival of Madden-Julian Oscillation (MJO) convective phase. The authors focus on this dipolar pattern over Borneo during the boreal winter MJO event in January-February 2017 using cloud-permitting modeling, observation and reanalysis datasets. The diurnal cycles of precipitation are analyzed during the local growing and decaying stages of this MJO event. Both the observation and simulation show positive precipitation anomaly at the southwestern Borneo and negative anomaly at the northeastern Borneo associated with the MJO easterly in the growing stage, while the pattern reverses in the decaying stage. Due to relatively high terrain, the low-level flows over Borneo split near the topography on the diurnal time scale. During the late afternoon and night (17–20 Local Solar Time), the splitting flow induced wake vortices and thermally-driven sea breezes tend to converge at the leeside, both contributing to leeward convergence and precipitation, which peaks at the midnight. Subsequent offshore propagation during midnight and early morning develops from the leeward inland convection, and propagates northwestwards in the growing stage on the west Borneo, and eastwards in the decaying stage on the east Borneo. Offshore propagation lasts until next noon when sea breezes and island convection initiate. The timing and location of the offshore propagation suggest that it is not an independent convective mode. Instead, it is tied to the dipolar distribution of island precipitation modulated by MJO.
Ferroelectric Liquid Crystal Dammann Grating by Patterned Photoalignment

In this article, a ferroelectric liquid crystal (FLC) dammann grating (DG) is demonstrated based on the patterned photoalignment technology. By applying low electric field (10 V) on the FLC DG, the grating can switch between a diffractive state with 7 × 7 optical spots array and a non-diffractive state, depending on the polarity of electric field. The FLC DG shows very fast switching speed with switching on time and off time to be only 81 μs and 59 μs respectively. Comparing with other fast LC DGs such as the ones based on blue phase LC or dual-frequency LC, the switching speed of the proposed FLC DG is about one order faster, which provides great potential and perspective for the FLC DG to be applied in a broad range of optical applications such as optical communication and beam shaping.
Effect of Temperature & Supply Voltage Variation on the Stability of Existing 7T SRAM Cell

In this paper an existing seven transistor (7T) CMOS SRAM cell stability is measured. N-curve method is used to find the stability of the cell. The stability parameters i.e. Static Voltage Noise Margin (SVNM), Static Current Noise Margin (SINM), Write Trip Voltage (WTV) and Write Trip Current (WTI) are measured by varying temperature and supply voltage. The existing cell has an inbuilt mechanism for charge sharing. This technique . for the write operation. The existing 7T SRAM cell has achieved 22.71% increase in stability as compared to reference cell, which validate the desired design approach.
Psychometric Properties of the Short Form-36 (SF-36) in Parents of Children with Mental Illness

Given the stressful experiences of parenting children with mental illness, researchers and health professionals must ensure that the health-related quality of life of these vulnerable parents is measured with sufficient validity and reliability. This study examined the psychometric properties of the SF-36 in parents of children with mental illness. The data come from 99 parents whose children were currently receiving mental health services. The correlated two-factor structure of the SF-36 was replicated. Internal consistencies were robust (α > 0.80) for all but three subscales (General Health, Vitality, Mental Health). Inter-subscale and component correlations were strong. Correlations with parental psychopathology ranged from r = −0.32 to −0.60 for the physical component and r = −0.39 to −0.75 for the mental component. Parents with clinically relevant psychopathology had significantly worse SF-36 scores. SF-36 scores were inversely associated with the number of child diagnoses. The SF-36 showed evidence of validity and reliability as a measure of health-related quality of life in parents of children with mental illness and may be used as a potential outcome in the evaluation of family-centered approaches to care within child psychiatry. Given the relatively small sample size of this study, research should continue to examine its psychometric properties in more diverse samples of caregivers.
Dietary-Lifestyle Patterns Associated with Bone Turnover Markers, and Bone Mineral Density in Adult Male Distance Amateur Runners—A Cross-Sectional Study

Background: Excessive mileage can be detrimental to bone mineral density among long-distance runners. The negative effects of mileage could be alleviated by appropriate nutrition. The purpose of this study was to analyse the dietary-lifestyle patterns in relation to bone mineral density and bone turnover markers among amateur marathoners. Methods: A total of 53 amateur male distance runners were divided into two clusters by k-means cluster analysis. Bone mineral density was measured by dual X-ray absorptiometry (DXA). Blood was drawn to analyse bone resorption marker C-terminal telopeptide (cTX) and bone formation marker amino-terminal propeptide of type I collagen (PINP). Food frequency intake and lifestyle information were measured by multicomponent questionnaire KomPAN®. Yearly average mileage per month was taken from each participant. Results: There were two distinguished clusters: Less-healthy-more-active-low-Z-score (LessHA) (n = 33) and More-healthy-less-active-high-Z-score (MoreHLA) (n = 20). LessHA had a lower frequency intake of pro-healthy food groups, a lower number of meals during a typical day, and a higher mileage training than the group of athletes who followed the MoreHLA. Athletes following the LessHA pattern also had a lower Z-score in the lumbar spine and femoral bone and a lower PINP. Conclusion: The current study suggests that pro-healthy dietary patterns and lower mileage may favour higher bone mineral density in male amateur marathoners.
Throughput Maximization for Wireless Powered Buffer-Aided Successive Relaying Networks

In this paper, we consider a wireless powered cooperative network, where two energy-constrained buffer-aided relays harvest energy from the source to assist the information delivery to the destination. To improve the spectral efficiency, a wireless powered successive relaying (WPSR) protocol is proposed to allow both relays to receive the information from the source and forward the buffered data to the destination, taking into consideration of the inter-relay-interference (IRI). We first reveal the impact of data buffers and energy storages at the relays by the achievable throughput comparison of the WPSR scheduling schemes with and without considering data buffers and energy storages. Our analysis validates the great potential of the buffer-and-forward data transmission and the harvest-store-use energy management strategies. Then, the network throughput is maximized via adaptive time and power allocation subject to the stability constraints of data buffers and energy buffers, and an adaptive wireless powered buffer-aided successive relaying (WPBSR) scheduling scheme is proposed. The proposed scheme approaches the optimal throughput of the wireless powered successive relaying network with bounded delay and finite length of data and energy buffers. Numerical results validate the analysis and noticeable spectral efficiency gain.
The Local Branching as a Learning Strategy in the Evolutionary Algorithm: The Case of the Set-Union Knapsack Problem

—In this paper, we introduce the local branching as a learning strategy for approximately solving the set-union knapsack problem; that is an NP-hard combination optimization problem. The designed method is based upon three features: (i) applying a swarm optimization for generating a set of current particles, (ii) using an iterative search for providing a series of diversified solutions linking some particles of the population and, (iii) injecting a local branching as a learning strategy for enhancing the global best solution: it can be viewed as a driving strategy employed for guiding particles towards the best position. The performance of the method is evaluated on benchmark instances of the literature, where its provided bounds are compared to those reached by the best methods available in the literature. New bounds have been discovered.
A coupled‐fed loop antenna for metal‐rimmed mobile phone applications

This letter presents a very simple coupled‐fed loop antenna for metal‐framed mobile phones, which is capable of providing hepta‐band WWAN/LTE operation. By utilizing the metal rim as a part of antenna, the proposed antenna only occupies a 14.5 × 8 mm2 area of the no‐ground portion. The proposed antenna consists of two coupled‐fed loops mainly composed of parts of the metal rim with two slits, a high‐pass matching circuit, and a distributed inductor. The lower band is initially provided by the quarter‐wavelength resonant mode of the longer coupled‐fed loop and further widen by a high‐pass matching circuit. The upper band is attained by the higher‐order mode of the longer coupled‐fed loop along with the quarter‐wavelength mode of the shorter coupled‐fed loop. Consequently, the generated two wide bands can cover the 824–960 and 1710–2690 MHz simultaneously. Besides, an antenna prototype is manufactured as well as tested, and the good measured antenna performances including reflection coefficient, radiation pattern, antenna gain and efficiency indicate that the proposed antenna is suitable for the metal‐frame mobile phone applications. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 59:371–377, 2017
Stress Relaxation Behavior of Additively Manufactured Polylactic Acid (PLA)

In this work, the stress relaxation behavior of 3D printed PLA was experimentally investigated and analytically modeled. First, a quasi-static tensile characterization of additively manufactured samples was conducted by considering the effect of printing parameters like the material infill orientation and the outer wall presence. The effect of two thermal conditioning treatments on the material tensile properties was also investigated. Successively, stress relaxation tests were conducted, on both treated and unconditioned specimens, undergoing three different strains levels. Analytical predictive models of the viscous behavior of additive manufactured material were compared, highlighting and discussing the effects of considered printing parameters.
Smart Surveillance System using Thing Speak and Raspberry Pi

This paper proposes the Smart Surveillance System using Thing speak and Raspberry pi. This design is a small portable monitoring system for home and office security. The model uses hardware mechanism such as Raspberry pi (model B), Gyro sensor and Raspberry pi camera. This system will monitor when motion detected, the Raspberry Pi will control the Raspberry Pi camera to take a picture and sent out an alert email with the image to the user by using Wi-Fi adaptor according to the program written in python environment. And at the same time the sensor real time data is visualized in the form of charts in Thing speak. The proposed system will works in standalone mode without the requirement of PC once programmed.
Enabling ethane as a primary gas turbine fuel: an economic benefit from the growth of shale gas

3 Introduction . 3 Fuel Supply Dynamics . 3 Hydrocarbon Sources and Characteristics . 5 Combustion System Considerations . 6 Evaluating Fuels . 6 GE’s Gas Turbine Combustion Systems . 8 Non-Methane Hydrocarbon Capability . 9 Economic Value of Alternative Fuels . 9 Conclusions . 10 References . 11
MEMS direction finding acoustic sensor

Conventional directional sound sensing systems employ an array of spatially separated microphones to achieve directivity. However, there are insects such as the Ormia ochracea fly that can determine the direction of sound using a miniature hearing organ much smaller than the wavelength of sound it detects. The fly's eardrums are coupled mechanically with a separation of only 0.5 mm and yet have a remarkable sensitivity to the direction of sound. The MEMS based sensor mimicking the fly’s hearing system was fabricated using an SOI substrate with a 25 μm device layer. The sensor consists of two 1.5 mm x1.6 mm wings connected in the middle by a 2.7 mm x 30 μm bridge. The entire structure is connected to the substrate by two torsional legs at the center. The frequency response of the sensor showed two resonance frequencies at approximately 1.1 kHz (rocking) and 1.5 kHz (bending). The resonance at 1.1 kHz is due to rocking of the wings by twisting the legs and the other at 1.5 kHz is due to bending of the bridge. The response of the sensor was probed electronically using comb finger capacitors integrated to the edges of the wings and with the help of an MS3110 chip. A peak output voltage of about 9V/Pa was measured for sound incident normal to the device at the resonance frequency of the bending mode. The bearing of the incident sound under these conditions could be determined to within a few degrees. These findings indicate the potential use of the MEMS sensor to locate sound sources with high accuracy.
LSTM network: a deep learning approach for short-term traffic forecast

Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.
A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures

This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen; as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen’s useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback–Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen’s work.
Hybrid Zero Dynamics Control for Gait Guidance of a Novel Adjustable Pediatric Lower-Limb Exoskeleton

Exoskeleton technology has undergone significant developments for the adult population but is still lacking for the pediatric population. This paper presents the design of a hip–knee exoskeleton for children 6 to 11 years old with gait abnormalities. The actuators are housed in an adjustable exoskeleton frame where the thigh part can adjust in length and the hip cradle can adjust in the medial-lateral and posterior-anterior directions concurrently. Proper control of exoskeletons to follow nominal healthy gait patterns in a time-invariant manner is important for ease of use and user acceptance. In this paper, a hybrid zero dynamics (HZD) controller was designed for gait guidance by defining the zero dynamics manifold to resemble healthy gait patterns. HZD control utilizes a time-invariant feedback controller to create dynamically stable gaits in robotic systems with hybrid models containing both discrete and continuous dynamics. The effectiveness of the controller on the novel pediatric exoskeleton was demonstrated via simulation. The presented preliminary results suggest that HZD control provides a viable method to control the pediatric exoskeleton for gait guidance.
Coordinated Optimization Control System of Big Data Intelligent Production Line

A mature coordinated and optimized control system can bring timeliness and benefits to the production line. This paper firstly exemplifies the big data intelligent production line coordination control system model and algorithm. They, respectively, include the intelligent production line coordination control system model, the big data intelligent coordination control model, and the hardware key electrical control system model of the air conditioning assembly big data intelligent production line, taking the tea production line as an example. The parameters of the coordinated control model are identified by the algorithm and system design, which considers and adjusts the functional requirements of the system to maintain the stable operation of the software system and store and transmit efficiently. To achieve efficient fit between functional requirements and modules, it created a complete assembly automation production line that meets actual needs. Then, the experiment of big data intelligent coordination and optimization control system based on cement clinker production line is carried out, and the intelligent coordination control system of cement clinker is designed. Compared with the data, it can be seen that in the coordinated control mode, the fluctuation of the temperature and pressure of the main steam is smaller, which can make the entire power generation system operate safely and stably. Based on the fuzzy control of the waste heat boiler and steam turbine load coordination control requirements, the reasonable coordination control of the boiler system and the generator set is carried out through fuzzy control, and the optimization and adjustment are carried out. Finally, the comparison and analysis of fuzzy control and traditional control experiments are carried out, and a coordinated optimization system using fuzzy control is obtained. It requires less feeding material, consumes less energy in the production process, generates more power, and can increase the average income by 4.87%, which has a good practical application prospect.
Preprocessing Pipelines for EEG

(Use the Microsoft Word template style: Author Email) or (Use Times New Roman Font: 10 pt, Italic, Centered) Electroencephalogram (EEG) signals collected, present a lot of challenges in order to process the data. Usually, the signals collected contain a lot of artifacts and noises. To address this issue and to make the preprocessing method easier and automated. EEG is widely used to record brain signals and activity for clinical and research purposes. EEG signals are the best way to understand brain signals compared to other methods because of how accurate it is. However, it comes with certain setbacks like being highly sensitive to noise and susceptible to artifacts. Hence developing a pre-processing method ensures a smooth understanding of the signals. These pre-processing methods include filtering and noise removal techniques. Section 1 includes the pre-processing pipelines that have been popularly used by researchers during this study. Section 2 consists of the results and comparisons of various pipelines and our understanding of what is more effective.
Rabies Virus Exploits Cytoskeleton Network to Cause Early Disease Progression and Cellular Dysfunction

Rabies virus (RABV) is a cunning neurotropic pathogen and causes top priority neglected tropical diseases in the developing world. The genome of RABV consists of nucleoprotein (N), phosphoprotein (P), matrix protein (M), glycoprotein (G), and RNA polymerase L protein (L), respectively. The virus causes neuronal dysfunction instead of neuronal cell death by deregulating the polymerization of the actin and microtubule cytoskeleton and subverts the associated binding and motor proteins for efficient viral progression. These binding proteins mainly maintain neuronal structure, morphology, synaptic integrity, and complex neurophysiological pathways. However, much of the exact mechanism of the viral-cytoskeleton interaction is yet unclear because several binding proteins of the actin-microtubule cytoskeleton are involved in multifaceted pathways to influence the retrograde and anterograde axonal transport of RABV. In this review, all the available scientific results regarding cytoskeleton elements and their possible interactions with RABV have been collected through systematic methodology, and thereby interpreted to explain sneaky features of RABV. The aim is to envisage the pathogenesis of RABV to understand further steps of RABV progression inside the cells. RABV interacts in a number of ways with the cell cytoskeleton to produce degenerative changes in the biochemical and neuropathological trails of neurons and other cell types. Briefly, RABV changes the gene expression of essential cytoskeleton related proteins, depolymerizes actin and microtubules, coordinates the synthesis of inclusion bodies, manipulates microtubules and associated motors proteins, and uses actin for clathrin-mediated entry in different cells. Most importantly, the P is the most intricate protein of RABV that performs complex functions. It artfully operates the dynein motor protein along the tracks of microtubules to assist the replication, transcription, and transport of RABV until its egress from the cell. New remedial insights at subcellular levels are needed to counteract the destabilization of the cytoskeleton under RABV infection to stop its life cycle.