Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

Gait phase recognition is an effective method of analyzing human motion and behavior that can be very meaningful in people’s daily life, especially when struggling with assisted rehabilitation. In this paper, a new algorithm that can recognize a human gait phase more accurately is proposed. The new gait phase recognition algorithm is based on a deep memory convolutional neural network (DM-CNN) using multiple sensor fusion. We used the plantar pressure sensor array and acceleration sensor array gait data, and then extracted the gait features using the DM-CNN. The measured data of the continuous gait cycle were divided into unit steps, and the data were analyzed and preprocessed. Then, a feature map of each sensor array was extracted by constructing a separate DM-CNN. Finally, each feature map was combined into a fully connected network, and a memory function was introduced to simulate historical behavior. We then tested the algorithm on the phases of a gait cycle and compared the evaluation indicators of each phase. In the experiment, we compared single-mode and multimode recognition results, and compared those with the new hidden Markov model (N-HMM), K-nearest neighbor (KNN), and hidden Markov model (HMM) algorithms. The experimental results show that when the multisensor data are fused, the average recognition accuracy can reach 97.1%, which is higher than those of the other algorithms and improves the recognition of a human gait phase. The accurate recognition of human gait can provide a better theoretical basis for the design of exoskeleton robot control strategies.
Toward Development of a Vocal Fold Contact Pressure Probe: Bench-Top Validation of a Dual-Sensor Probe Using Excised Human Larynx Models

A critical element in understanding voice production mechanisms is the characterization of vocal fold collision, which is widely considered a primary etiological factor in the development of common phonotraumatic lesions such as nodules and polyps. This paper describes the development of a transoral, dual-sensor intraglottal/subglottal pressure probe for the simultaneous measurement of vocal fold collision and subglottal pressures during phonation using two miniature sensors positioned 7.6 mm apart at the distal end of a rigid cannula. Proof-of-concept testing was performed using excised whole-mount and hemilarynx human tissue aerodynamically driven into self-sustained oscillation, with systematic variation of the superior–inferior positioning of the vocal fold collision sensor. In the hemilarynx experiment, signals from the pressure sensors were synchronized with an acoustic microphone, a tracheal-surface accelerometer, and two high-speed video cameras recording at 4000 frames per second for top-down and en face imaging of the superior and medial vocal fold surfaces, respectively. As expected, the intraglottal pressure signal exhibited an impulse-like peak when vocal fold contact occurred, followed by a broader peak associated with intraglottal pressure build-up during the de-contacting phase. As subglottal pressure was increased, the peak amplitude of the collision pressure increased and typically reached a value below that of the average subglottal pressure. Results provide important baseline vocal fold collision pressure data with which computational models of voice production can be developed and in vivo measurements can be referenced.
Optimal Strategy of Active Distribution Network Considering Source–Network–Load

With the access of renewable distributed generations and the uncertainty of demand-side response, the operation states of the active distribution network (ADN) are uncertain. This study proposes a novel optimal strategy taking active management costs of the source–network–load as the goal and considering the site selection and the incentive price for the responsive load to optimise the ADN. To deal with uncertainties, a chance-constrained optimisation model is established and the Monte Carlo sampling method is used. Then, the uncertain model is settled by the improved backtracking search algorithm, which uses an adaptive mutation scale factor. Finally, the practicability of the proposed strategy and the performance of the algorithm are verified by the modified IEEE 33-bus distribution system and an actual distribution system in China.
A Ka-Band Scalable Hybrid Phased Array Based on Four-Element ICs

In this article, a scalable hybrid phased-array system is presented through synchronization, analog complex weighting, and digital beamforming of numerous fully integrated Ka-band four-receiver (RX)/four-transmitter (TX) phased-array transceiver integrated circuits (ICs). A 1.09-GHz clock synchronizes the local oscillator (LO) and a 50-MHz clock synchronizes analog-to-digital (A/D)/digital-to-analog (D/A) converters for all array elements. Phase shifting is first accomplished in the analog domain using optimal intermediate frequency (IF)/LO complex weighting and signal summing in the 4RX/4TX IC to reduce the number of signals by a factor of four, followed by A/D sampling and digital beamforming in field-programmable gate arrays (FPGAs) and central processing units (CPUs). Phase-shifting properties, programmable gain variations, and antenna patterns of each RTX channel are measured and tabulated to calculate the optimal channel weights. The long-term phase stability is enhanced through temperature control by monitoring all ICs’ temperatures in real time and adaptively adjusting the duty cycle of the TX mode of each IC to limit instantaneous temperature variations to ±0.5 °C over each calibration session. This reduces random phase errors from 13.3° to 4.8° in the TX mode. After each Vivaldi antenna is located on a 2-D rectangular grid, an <inline-formula> <tex-math notation="LaTeX">$8 \times 4$ </tex-math></inline-formula> subarray module with synchronized digital output is demonstrated. With the boresight pointing along the <inline-formula> <tex-math notation="LaTeX">$\hat {x}$ </tex-math></inline-formula>-axis, the eight-element dimension pointing along the <inline-formula> <tex-math notation="LaTeX">$\hat {y}$ </tex-math></inline-formula>-axis, and the four-element dimension pointing along the <inline-formula> <tex-math notation="LaTeX">$\hat {z}$ </tex-math></inline-formula>-axis, this subarray steers radiation patterns with the <inline-formula> <tex-math notation="LaTeX">$\overrightarrow {E}$ </tex-math></inline-formula>-field polarized to the <inline-formula> <tex-math notation="LaTeX">$\hat {y}$ </tex-math></inline-formula>-axis between ±40° in both azimuth and elevation with 13.4° and 26.4° measured 3-dB beamwidths, respectively.
A Dual Half-Bridge Converter With Hybrid Rectifier for DC Power Supply in Railway Systems

Dual half-bridge (DHB) converter is a recent innovation in the family of full-bridge converters, and it can achieve higher efficiency compared to the traditional converters. The DHB converters with center-tapped rectifier (CTR) and full-bridge rectifier (FBR) have been adopted in low (<100 V) and high (>200 V) output voltage applications, respectively. However, the output voltage is between 100 and 200 V in some applications, such as auxiliary railway dc power supply, both CTR and FBR can be the candidate and it is very indistinct to select the matched rectifier. To suit for such specific application, this article proposes a DHB converter with hybrid rectifier (Hyb-R). The Hyb-R is composed of a CTR section and an FBR section, which are connected in series by sharing two diodes. As a combined structure, the voltage stress of Hyb-R is between that of FBR and CTR, which makes Hyb-R well appropriate for dc power supply in railway systems. In this article, the operational principle and performance of the proposed converter are analyzed in depth. Three prototypes based on the DHB converters with CTR, FBR, and Hyb-R are built and tested, respectively. Experimental results show that the proposed converter can achieve a superior efficiency compared with other converters.
Review of Machine Learning Based Remaining Useful Life Prediction Methods for Equipment

： With the development of science and technology as well as the advancement of production technology, contemporary equipment is increasingly developing towards large-scale, complex, automated and intelligent direction. In order to ensure the safety and reliability of equipment, the remaining useful life (RUL) prediction technology has received widespread attention and been widely used. Traditional statistical data-driven methods are obviously influenced by the choice of models. Machine learning has powerful data processing ability, and does not need exact physical models and prior knowledge of experts. Therefore, machine learning has a broad application prospect in the field of RUL prediction. In view of this, the RUL prediction methods based on machine learning are analyzed and expounded in detail. According to the depth of machine learning model structure, it is divided into shallow machine learning methods and deep learning methods. At the same time, the development branches and research status of each method are sorted out, and the corresponding advantages and disadvantages are summarized. Finally, the future research directions of RUL prediction methods based on machine learning are discussed.
TELEPHONE SUPPORT ON THE POSTPARTUM DEPRESSION AFTER NORMAL VAGINAL DELIVERY

Background & Aims : The postpartum period is a dangerous potential time for postpartum depression. Postpartum support is a way to reduce postpartum depression. The purpose of the current study is to determine the effectiveness of postpartum supports on the postnatal women depression after childbirth. Materials & Methods : This study, a semi experimental study, was conducted in Marand Hospital. Two hundred sixty mothers agreed to take part in the study, and they were randomly allocated into two goups of intervention group (n = 130) or control group (n = 130). The intervention group received twice telephone counseling for 20- minute at the first week, and from second till 6th weeks, they received one telephone counseling at week plus routine care. The control group received only the routine care. Also for evaluation of the mother’s unanticipated needs, a 24-hour telephone access was provided to the researcher. The main outcome measurement instruments included recording demographics data sheet, postpartum problem’s checklist, and the Edinburgh Postnatal Depression Scale (EPDS) which was administered by a trained interviewer six weeks after delivery. Control group received general postpartum education. The data were analyzed by (cid:548) ², Fisher test and T test SPSS Version 11.5. Results: At 6 week postpartum, mean of depression in the intervention group was ( 15/80 ± 5/12), and in the control group, it was (18/43 ± 5/90 ). On the EPDS scale, in the intervention group, there was a significant decrease comparing to control group ( P < 0.0001) . Conclusion: The result of this research showed that postpartum support by midwife could decrease women’s postpartum depression in the pureperium.
Design and fabrication of home automation

The objective of this project is to control all electronic device in the house, remotely and to automate some parts of the house like pipes, garage using Arduino or raspberry pi. The raspberry pi is a microprocessor which can be used for multiple purposes like getting input signal, based on the signal the output is calculated and sent to the actuators. Here we use raspberry pi board, few dc motors (this motor works on dc using permanent magnet), LED lights and a model of 3d model of the house. The prototype model build using 3d printer and electrical bulb, doors, fan and washing machine are demonstrated. This project will be a real-world model which will have the ability to perform task of operating devices, remotely. This project will help the people to keep an eye on their house when they are not home and help to on or off some of the appliances when they are away from home.
With protocells, scientists probe the chemistry that started biology

Unfortunately for scientists, there is no videotape of the universe’s history that they can rewind to watch how life got started on Earth. Instead, they must recapitulate life in the lab to find plausible models of what happened billions of years ago. They start with chemical mixtures that might have formed under conditions on early Earth and see how those scenarios play out. They especially want to figure out how cells might have formed. For example, scientists have made significant progress in showing that potential building blocks of life can spontaneously form from simple mixtures. But building blocks alone do not equal life. Scientists are now trying to figure out how those building blocks could assemble into something we might recognize as an early cell. “There might be many conditions that give you suitable building blocks,” says Wilhelm T. S. Huck, a researcher at Radboud University who is trying to
Three‐dimensional printing technologies for terahertz applications: A review

Three‐dimensional (3D) printing technologies enables fast prototyping of complex 3D objects with ever improving printing qualities. To date, 3D printing has been found useful in areas such as manufacturing, industrial design, aerospace, dental and medical industries, and many others. In this article, we review recent advances of 3D printing technologies for terahertz (THz) applications. Different 3D printing technologies and printable materials are first discussed and compared. 3D‐printed THz components and devices, which are categorized as waveguides/fibers, antennas, and quasi‐optical components, are further demonstrated. It is found that the performances and functionalities of 3D‐printed THz devices have been greatly enhanced, while the operating frequencies have been increased from the lower end of THz range to over 1 THz region. With further development of novel materials and printing techniques, it is believed that 3D printing technologies will play an important role in the realization of THz components for efficient control and manipulation of THz waves.
On the free-volume model of multi-shape memory effect in amorphous polymer

Free volume theory, which is a popular approach to solve the problems associated with glass transition in polymers, is applied to investigate multi-shape memory effect (multi-SME) of amorphous shape memory polymers (SMPs) as well. To understand the phenomena of multiple shape recovery behaviors, theories about the SME and multi-SME in amorphous SMPs are firstly developed. The thermodynamics in the SMPs with dual- and triple-SME is then investigated in the frameworks of the free volume model and Eyring equation, taking account of the dependencies of free volume and activation energy on relaxation time. Finally, the proposed free volume model is evaluated and compared with results from the amorphous SMPs with dual- and triple-SMEs. Results from our newly proposed model show a good agreement with experimental results obtained at different temperatures.
SUBJECTIVE WELL-BEING IN NURSING AND MIDWIFERY STUDENTS OF GOLESTAN UNIVERSITY OF MEDICAL SCIENCES

Background and Objective: Subjective well-being, a field of positive psychology, attempts to assess the cognitive and emotional lives. Given the fact that nursing and midwifery students are exposed to stress in clinical settings, we aimed to assess subjective well-being to promote educational programs. Material and Methods: This across-sectional study was conducted on 104 nursing and Midwifery students selected via simple random sampling, 2013. The instruments, filled out by students, were a demographic checklist and Kyeez subjective well-being questionnaire consisting of three domains of emotional, social and psychological. We used mean and standard deviation for data description and T-test and ANOVA for analysis. Results: The mean score of subjective well-being was 203.20 in nursing and 210.21 in midwifery students. In nursing students, the Mean score in emotional, psychological and social domain was 44.05, 93.40 and 65.74, respectively, and it was 45.72, 94.78 and 69.55, respectively in midwifery students. Gender was the only variable associated significantly with subjective well-being of nursing students (P>0.05). Conclusion: Given the high score of subjective well-being and that of its domains, we recommend applying some educational programs with the consideration of emotional, psychological and social aspect of students.
Monitor Cotton Budding Using SVM and UAV Images

Monitoring the cotton budding rate is important for growers so that they can replant cotton in a timely fashion at locations at which cotton density is sparse. In this study, a true-color camera was mounted on an unmanned aerial vehicle (UAV) and used to collect images of young cotton plants to estimate the germination of cotton plants. The collected images were preprocessed by stitching them together to obtain the single orthomosaic image. The support-vector machine method and maximum likelihood classification method were conducted to identify the cotton plants in the image. The accuracy evaluation indicated the overall accuracy of the classification for SVM is 96.65% with the Kappa coefficient of 93.99%, while for maximum likelihood classification, the accuracy is 87.85% with a Kappa coefficient of 80.67%. A method based on the morphological characteristics of cotton plants was proposed to identify and count the overlapping cotton plants in this study. The analysis showed that the method can improve the detection accuracy by 6.3% when compared to without it. The validation based on visual interpretation indicated that the method presented an accuracy of 91.13%. The study showed that the minimal resolution of no less than 1.2 cm/pixel in practice for image collection is necessary in order to recognize cotton plants accurately.
Manejo de la disfunción autonómica en la enfermedad de Parkinson

El temblor, la rigidez, la bradicinesia y la perdida de reflejos posturales son los signos cardinales de la enfermedad de Parkinson (EP) [1]. Sin embargo, pueden presentarse sintomas tan diversos como hiposmia, trastornos del sueno REM, deterioro cognitivo, depresion, ansiedad y apatia [2], y trastornos secundarios a la disfuncion del sistema autonomo, como estrenimiento, incontinencia urinaria, disfagia, sialorrea, disfuncion erectil, hipotension ortostatica y posprandial, hipertension supina, dishidrosis y alteraciones en la termorregulacion [3,4]. Hasta un 21% de los pacientes con EP presenta como primera manifestacion un sintoma no motor [5]. La prevalencia de la disfuncion autonomica es variable segun el estudio (14-80%) [6], con un impacto negativo en la calidad de vida ya desde el inicio de la enfermedad [7]. Los tratamientos para muchos de estos problemas carecen de suficiente evidencia cientifica como para poder recomendarse [8]. Nuestro objetivo es revisar las caracteristicas fisiopatologicas y clinicas de la disfuncion autonomica en la EP y aportar un enfoque practico de su manejo segun la evidencia disponible (Tabla I) [9].
Dielectric Barrier Discharge Treatment of Granular Plastic Mixtures in View of Their Triboelectrostatic Separation

Successful electrostatic separation of granular plastic mixtures is strongly conditioned by the effective triboelectric charging of their constituents. The aim of this article is to demonstrate that appropriate exposure to a dielectric barrier discharge (DBD) at atmospheric pressure changes the triboelectric properties of plastics and favors their separation under the action of electric field forces. The experiments were conducted with nine different plastics originating from waste electric and electronic equipment [acrylonitrile butadiene styrene, high-impact polystyrene (HIPS), polycarbonate (PC), Cristal PC, polyethylene (PE), polypropylene (PP), polystyrene (PS), Cristal PS, and polyvinyl chloride (PVC)] using a rotating cylinder tribocharging device and a roll-type electrostatic separator. The cylinder of the device was made of either high-density PE or PVC. The comparison between the results of triboelectric charging and electrostatic separation of DBD-exposed and unexposed granules pointed out the effectiveness of nonthermal plasma treatment of the plastic wastes. Most of DBD-exposed granules acquire a negative charge, whereas the majority of the plastics considered in this study got positively charged, if not pretreated in the nonthermal plasma reactor. The modifications the DBD exposure induces on the triboelectric series highly affect the electrostatic separation of six different mixtures (PE–HIPS, PE–PP, PE–PVC, PP–HIPS, PP–PVC, and PVC–HIPS).
An Efficient File Hierarchy Attribute Based Encryption Using Optimized Tate Pairing Construction in Cloud Environment

Abstract One of the most preferred encryption techniques for data sharing in cloud environment is Ciphertext-Policy Attribute Based Encryption (CP-ABE). An efficient File Hierarchy CP-ABE (FH-CP-ABE) scheme using an integrated access structure was proposed by Wang and colleagues. Their working construction was based on symmetric pairing. In this article, we propose an efficient Asymmetric File Hierarchy Ciphertext-Policy Attribute Based Encryption (AFH-CP-ABE) which is based on an asymmetric pairing construction using denominator elimination and twist mapping. We apply this optimized Tate pairing in the proposed AFH-CP-ABE to find out its computational costs for encryption and decryption using gmp 6.0. 0 library and then compared with existing schemes.
Weather division‐based wind power forecasting model with feature selection

The sample division-based hybrid model is an enforceable approach to improve wind power forecasting accuracy in the short term. These models up to now prefer to keep the input same for all the individual schemes, which weaken the effort of division and restrict the further improvement of the accuracy. To this end, a weather division-based wind power forecasting model with ensemble feature selection is proposed for refinement. The methodology comprises three stages: the division of wind power associated weather based on hierarchical clustering with the DTW distance metric, ensemble feature selection framework considering both predictive accuracy and stability, and wind power prediction based on machine learning algorithms for each weather type. As a test case, the proposed methodology is applied to the data of a wind farm group in Northwest China. With respect to the single models, the proposed method has improved the predictive accuracy by up to 30% at three error metrics, and the weather associated features are discussed.
Development of Doped Silicon Multi-Element Stress Sensor Rosette With Temperature Compensation

In this paper, a developed n-type piezoresistive three dimensional (3D) stress sensor with full temperature compensation is presented. The proposed sensing rosette benefits from the stress insensitivity of the full-circular n-type piezoresistor, oriented over (111) silicon plane, to detect only temperature changes for compensation. Moreover, the unique behavior of shear piezoresistive coefficient ( $\pi _{{{44}}}$ ) of n-Si is utilized to construct a piezorestive stress sensing rosette over (111) silicon plane that is capable of extracting 3D stress components. A prototype stress sensing chip was microfabricated to test the capability of the developed sensing rosette to accurately extract stress applied on structures at different thermal environments. The fabricated sensing chip was subjected to different mechanical loads using a loading rig with four-point bending fixture. The testing was carried out over a temperature range of 0–50 °C. The results showed that the proposed sensing chip has the capability of capturing the stress applied at different temperatures. Also, the developed sensing rosette showed less sensitivity to the uncertainty in piezoresistive coefficients’ values compared to the other developed 3D piezoresistive stress sensors.