Miracle-dz/UniFAR-FTUs huggingface dataset:
13 Modulation of Steroid Receptor Signal Transduction by Heat Shock Proteins

I. INTRODUCTION The 90-kD heat shock protein (hsp90) associates with numerous cytosolic and nuclear proteins involved in cell signaling, including certain steroid hormone receptors (Joab et al. 1984; Catelli et al. 1985; Sanchez et al. 1985; Wilhelmsson et al. 1990), tyrosine kinases (Oppermann et al. 1981; Lindquist and Craig 1988), serine-threonine kinases (Rose et al. 1987; Matts and Hurst 1989; Miyata and Yahara 1992; Stancato et al. 1993), and actin (Koyasu et al. 1986) and tubulin (Sanchez et al. 1988); in addition, hsp90 interacts with hsp56 and hsp70 (Sanchez et al. 1990a; Perdew and Whitelaw 1991). The association of hsp90 with steroid hormone receptors has been most thoroughly studied and is the central focus of this chapter. We suggest that hsp90 may bind and alter the activity of a large class of cellular signaling proteins, thereby playing a common role in their function and regulation. Members of the “nuclear receptor” superfamily respond to their cognate ligands, such as steroids, by binding to specific DNA sequences and altering the transcriptional activity of adjacent promoters (Yamamoto 1985; Evans 1988). Aspects of the signal transduction pathway that triggers these alterations are schematized in Figure 1. In their unliganded, inactive state, certain of these receptors are components of hetero-oligomers, which we term aporeceptor complexes. An aporeceptor complex consists minimally of a receptor monomer, an hsp90 dimer, and an hsp56 monomer (Table 1) (Rexin et al. 1991; Rehberger et al. 1992). One function of the complex is to recognize and bind the cognate ligand. Concomitant with.
Smoothing PV Power Fluctuations with Electric Vehicle and its Grid Interaction

The fluctuations in PV power due to weather or passing clouds can reduce the stability and reliability of the solar power generated by grid interfaced PV system. This paper presents a grid interfaced photovoltaic (PV) and Electric Vehicle (EV) integration to smooth PV power fluctuations. A modified Moving Window Average (MWA) is used to smooth the PV power fluctuations. In this proposed method, a smooth PV power is estimated for 15 minutes time step. The difference between the predicted output power and the actual PV generation is provided by the EVs thus making the aggregated output power smooth. Real time one second interval solar data collected from the solar installation at the University of California, Los Angeles (UCLA) is used for testing the proposed method. The EV data is taken from UCLA, EV garage charging points. The smoother PV power for 15 min is considered from energy market prospective with its benefits to consumers. An evaluation based on comparative performance is made between the proposed and existing methods. The EV scheduling based on its availability and energy cost is presented for the cases of fully cloudy days and night time. During operation EVs are charged/discharged from/to grid keeping EV user benefits as priority.
High-Quality Fault-Resiliency in Fat-Tree Networks (Extended Abstract)

Coupling regular topologies with optimized routing algorithms is key in pushing the performance of interconnection networks of HPC systems. In this paper we present Dmodc, a fast deterministic routing algorithm for Parallel Generalized Fat-Trees (PGFTs) which minimizes congestion risk even under massive topology degradation caused by equipment failure. It applies a modulo-based computation of forwarding tables among switches closer to the destination, using only knowledge of subtrees for pre-modulo division. Dmodc allows complete re-routing of topologies with tens of thousands of nodes in less than a second, which greatly helps centralized fabric management react to faults with high-quality routing tables and no impact to running applications in current and future very large-scale HPC clusters. We compare Dmodc against routing algorithms available in the InfiniBand control software (OpenSM) first for routing execution time to show feasibility at scale, and then for congestion risk under degradation to demonstrate robustness. The latter comparison is done using static analysis of routing tables under random permutation (RP), shift permutation (SP) and all-to-all (A2A) traffic patterns. Results for Dmodc show A2A and RP congestion risks similar under heavy degradation as the most stable algorithms compared, and near-optimal SP congestion risk up to 1% of random degradation.
Classification of ERP Signals from Mild Cognitive Impairment Patients with Diabetes using Dual Input Encoder Convolutional Neural Network

Mild Cognitive Impairment (MCI) is the early stage of Alzheimers Disease (AD), which is an irreversible neurological disease. Type II diabetes is also closely related to brain recognition, so it is extremely necessary to diagnose the patient's cognition as early as possible. Among them, the event-related potential (ERP) based on electroencephalogram (EEG) is an important research method. In order to diagnose the disease more accurately, machine learning has been widely used. However, deep learning still has a broad application space. Convolutional neural networks (CNN) is high-performance model for deep learning. In this study, we constructed a Daul input encoder convolutional neural network (DIE-CNN) for event related potential (ERP) data with type 2 diabetes. In experiments with multiple models, DIE-CNN performed optimally. The results show that DIE-CNN is an effective method to diagnose the condition of the subject, and the method also retains the expansion space to build a more complex comprehensive evaluation system.
Modeling and simulation of the first phase of the viscoelastic biological particles considering simultaneous roughness of tip, particle, and substrate

In recent years, atomic force microscopy as a basic tool for nanoparticles displacement and manufacturing of intended subjects from atoms and molecules has attracted researchers in different fields such as drug delivery, tissue engineering and etc. Manipulation simulation of the AFM nanorobot is a fundamental tool for controlled and accurate displacement of subjects and particles in different scales. Since passing from macro to micro and nanoscales causes the increased ratio of the area to volume consequently surface forces such as friction and adhesion become more important. According to the surface roughness in contact, critical time and force have been extracted in this paper considering particle and substrate roughness simultaneously to find out the effect of the surface roughness on critical parameters. Therefore, developed rough models for viscoelastic state have been used and asperities radius using experimental test on cancer cells has been extracted to increase simulation accuracy. Results show that the critical force for the elastic, rough viscoelastic and viscoelastic models have higher magnitudes respectively. This difference between elastic and viscoelastic models are completely obvious but between two viscoelastic models are less.
A Low Cost Bionic Arm Based on Electromyography Sensor

Over the past decade, the number of individuals suffering with disabilities is rising at an alarming rate due to the factors such as increase in population growth, ageing factors and also the occurrence of long-lasting diseases. In particular, limb amputation presents multi-directional challenges as it affects sensation of the body, function of the body, career, self-image and family relationships. To address this issue, bionic arms are suggested. Bionic arms operate by gathering signals from an amputee muscle. Though EMG based bionic arms are existing in the market, utmost of them are too costly to be economically accessible or it is overloaded with heavy weight for long time wearing. This proposed work demonstrates the construction and working of a cost effective, robust and flexible bionic arm controlled through Arduino microcontroller. The designed bionic arm consists of a 3–D printed hand material, a myoware sensor for monitoring muscle activity, an arduino processor, six motors and an extremely stretchable hand glove. The use of myoware sensor has allowed developing the bionic arm to realize the natural movements of the hand. The bionic arm was designed and implemented using 3-D printing and casted with silicone material. The low cost bionic arm has been tested in real time on amputees and results show that it can grasp various objects effectively.
A Multi-modal Approach for Emotion Recognition of TV Drama Characters Using Image and Text

Research on facial emotion recognition has long been popular for various purposes. This paper investigates the recognition of the character emotions, to assist in understanding the story. The goal of this research is to classify the facial images of the characters in the Korean TV series ‘Misaeng: The Incomplete’1 into 7 emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise. We built a multi-modal deep learning model which utilizes facial images as well as textual information describing the situations, to classify the facial images. Our experiments indicate that employing multi-modality enhances the performance of facial emotion recognition of story characters.We concludes with discussions and future work.
EEG Classification of Forearm Movement Imagery Using a Hierarchical Flow Convolutional Neural Network

Recent advances in brain-computer interface (BCI) techniques have led to increasingly refined interactions between users and external devices. Accurately decoding kinematic information from brain signals is one of the main challenges encountered in the control of human-like robots. In particular, although the forearm of an upper extremity is frequently used in daily life for high-level tasks, only few studies addressed decoding of the forearm movement. In this study, we focus on the classification of forearm movements according to elaborated rotation angles using electroencephalogram (EEG) signals. To this end, we propose a hierarchical flow convolutional neural network (HF-CNN) model for robust classification. We evaluate the proposed model not only with our experimental dataset but also with a public dataset (BNCI Horizon 2020). The grand-average classification accuracies of three rotation angles yield 0.73 (±0.04) for the motor execution (ME) task and 0.65 (±0.09) for the motor imagery (MI) task across ten subjects in our experimental dataset. Further, in the public dataset, the grand-averaged classification accuracies were 0.52 (±0.03) for ME and 0.51 (±0.04) for MI tasks across fifteen subjects. Our experimental results demonstrate the possibility of decoding complex kinematics information using EEG signals. This study will contribute to the development of a brain-controlled robotic arm system capable of performing high-level tasks.
In-Situ Calibration and De-Embedding Test Structure Design for SiGe HBT On-Wafer Characterization up to 500 GHz

In this paper, we present an in-situ thru-reflect-line (TRL) calibration and de-embedding kit that sets the reference plane in close proximity to the device under test. This is made possible thanks to the realization of the standards at the metal3 BEOL level, instead of the common meta1-8 solution. This novel calibration kit has been compared to classic TRL, both for parasitics assessment and by direct application on the active device (HBT) measurements.
Energy- and Cost-Efficient Physical Layer Security in the Era of IoT: The Role of Interference

IoT is emerging as the future evolution of the Internet, aiming to provide connectivity for everyone and everything. Since IoT is expected to carry important and private information, a high level of PHY security is critical for wireless communications in IoT, as a complement for traditional security techniques that are employed at high layers. In this overview, we examine the recent interest in energy-efficient and cost-efficient PHY solutions for securing downlink IoT transmission through interference exploitation. This exciting line of research departs from conventional interference cancellation, and judiciously employs the inherent interference as a useful element for LUs while obstructing the eavesdropping of information. We first discuss the concept of CI, and then elaborate the fundamental CI signal design that employs the traditionally undesired interference as a constructive element to LUs while ensuring they are destructive to potential Eves. Subsequently, we illustrate several low-hardware-cost techniques to inherit the advantage of CI in an energy- and cost-efficient manner, from the perspective of HBF and DM. This family of techniques brings a disruptive vision of interference management for securing wireless communications with an eye on low-cost and hardware-constrained devices tailored for IoT systems.
Linear vs. Nonlinear Modeling of Continuum Robotic Arms Using Data-Driven Method

Dynamics modeling of continuum robotic arms is of great importance due to the highly nonlinear characteristics, uncertain and complex structure, and the inherent underactuation. This affects further usage in various aspects, including inverse kinematics, trajectory generation, control and optimization. In this paper, a modelling approach is proposed through the use of data-driven identification by linear and nonlinear models known as ARX (autoregressive with exogenous terms) and NARX (nonlinear autoregressive with exogenous terms) models. The unknown parameters in the ARX model are the system parameters; while the structure is known. However, for NARX model, the whole structure is considered to be unknown. These two structures are used to model a single-section continuum robotic arm, and the results are compared. Finally, the advantages and disadvantages of them are discussed.
Falls among the elderly in rural areas of the Sharkia Governorate, Egypt

Amac: Dusmeler yaslilari etkileyen en onemli halk sagligi sorunlarindan biridir ve yaslilarda ciddi sonuclara yol acabilir. Bu calisma ile yasli bireyler arasinda dusme prevelansini ve iliskili risk faktorlerinin arastirilmasi amaclandi. Yontem: Kesitsel tipte planlanan calisma Misir’in Sharki ilinin kirsal bolgesinde 60 yasinda 408 yasli uzerinde yapildi. Calismaya katilanlara yapilandirilmis veri formu, antropometrik olcumler, denge ve yuruyus icin Tinette olcegi uygulandi. Bulgular: Yaslilar arasinda dusme prevelansi yuksektir (%62). Dusmelerin yarisi kapali alanda oldu, bunlarin da cogunlugu banyo (%38.2), merdivenlerde (%22.8) ve bodrumda (%21.1) idi. Dusme ve kayma dusmelerin cogunun nedeni idi. Bulgular ilac kullaniminin yanisira farkli kronik hastaliklarla yuksek riskli Tinetti skoru (OR=12.3) arasinda onemli bir iliski oldugunu gostermektedir. Sonuc: Dusmeler yaslilar arasinda sik gorulen bir problem olmasina karsin, burada onlenebilir risk faktorleri bulunmaktadir. Toplum ve aile hekimleri geriatric bakim programlarini uygulama, risk faktorlerinin tanimlanmasi ve dusmelerin taranmasi yoluyla dusmeleri onleme ve yonetmede onemli bir role sahiptir. Yasli, prevelans, dusmeler, risk faktorleri, Tinetti ocegi
Review On IoT Enabled Smart Cities in India

in the year 2015, the newly elected Political-party of India started the smart-cities project. The main purpose of enlightening the governance is corporal and governmental structures and facilities (e.g. constructions of buildings, roads, and power supplies, etc.) dearth that outbreak Indian cities. The Operation emphatically, conditions that there is no standard definition of a ‘smart city’ and indicates immeasurable freedom for cities to self-define their thoughtful of ‘smart-ness’. It is also the innovative footstep by the Indian Government to construct smart-cities in India to permit monetary development and eminence people’s life by permitting confined expansion and using smart technologies to make it’s resident’s life healthier. For this purpose, the government of India firstly covering five years plan for hundreds of cities in India. After five years the evaluation process takes care by the MoUD and fascinating the existence of smart cities criteria and also will apply outside of the cities. This paper presented the challenges on the operation, implementation strategy and achieving objectives of smart cities in India.
A Novel De Novo Missense Mutation in HNF4A Resulting in Sulfonylurea-Responsive Maturity-onset Diabetes of the Young

Maturity-onset diabetes of the young (MODY) is a monogenic subgroup of diabetes mellitus characterized by autosomal dominant inheritance, non-insulin diabetes onset usually before 25 years of age and decreased insulin production or secretion response to glucose. At least 13 different genes have been reported to be associated with MODY to date (1). Approximately 1-2% of patients with diabetes have a monogenic type (2). The inactivating mutations in the nuclear transcription factor 1 homeobox A (HNF1A), the hepatocyte nuclear factor 4 homeobox (HNF4A) and the glucokinase (GCK) are the most common causes of MODY (3). Other genes associated with MODY are infrequently detected: HNF1B, IPF, NEUROD, PDX1, KLF11, CEL, PAX4, BLK, ABCC8 and KCNJ11 (3). While the heterozygous inactivating mutations in the GCK gene lead to asymptomatic mild fasting hyperglycemia, mutations in the genes of HNF1A and HNF4A lead to progressive failure in insulin secretion and worsening of glucose tolerance with age (4). HNF4A is a member of the steroid/thyroid hormone receptor superfamily and plays a major role in glucose stimulated insulin secretion. Homozygous HNF4A mutation is lethal at the early embryonic stage (5). However, ABS TRACT Maturity-onset diabetes of the young (MODY) is a monogenic form of diabetes, with autosomal-dominant inheritance, which usually develops before 25 years of age. MODY is classically caused by a heterozygous mutation of genes known to affect insulin production or secretion. Heterozygous inactivating hepatocyte nuclear factor 4A (HNF4A) mutations, one of the rare subtypes of MODY, cause impaired insulin secretion and subsequent glucose intolerance especially in adolescence. Conversely, HNF4A mutations are also known to be associated with macrosomia and hyperinsulinemic hypoglycemia in newborns. Herein, we report a rare cause of diabetes resulting from a novel heterozygous mutation in the HNF4A gene. In conclusion, genetic testing should be considered in order to establish an accurate diagnosis and provide an opinion in determining the appropriate type of treatment.
Vector Control of a Switched Reluctance Motor

This paper dwells upon vector control over electromagnetic moment in a switched reluctance motor. The control task is intended to address ripple of the electromagnetic torque. The motor windings must carry sinusoidal currents while their inductance must show a sinusoid ripple of attaining that goal. The paper presents the design rotor-stator teeth ratios at which the rotor motion is accompanied by sinusoid ripple of inductance. The research team has conducted pattern synthesis of electromagnetic processes allowing to form sinusoidal currents in phase windings. The structure diagram is presented herein to show the controls of a switched reluctance motor. A standard frequency converter can be used to generate the voltage to be applied to stator windings.
Motor imagery signal classification based on transfer learning

The EEG of motor imagery varies greatly according to different subjects and the same subject in different time periods. Traditional machine learning methods can only solve the classification and recognition of the same individual within a short period of time, and the classification and recognition effect also depends on the difference of data sets, with strong individual differences. Many classification methods are unstable and have poor universality. Transfer learning can use knowledge from similar data to enhance the learning process, and use knowledge in related fields to help complete the learning tasks in the target field, so as to change the traditional learning from scratch into accumulated learning and improve learning efficiency. In this paper, the power spectrum characteristics of 8 channels signals related to motor imagery at 7-29hz were extracted, and the motor imagery data were classified and modeled by transfer learning algorithm. Meanwhile, compared with the other two existing classification methods PSD (Power Spectral Density) and CSP (Common Spatial Pattern), the analysis results showed that the classification accuracy of transfer learning (90.9 ± 2.2) was higher than that of traditional PSD+LDA(62.5±11.6) and CSP+SVM (71.3±3.5), which verified the feasibility of transfer learning in motor imagery BCI classification and recognition.
Visual Object Detection Based LiDAR Point Cloud Classification

The environmental perception plays a pivotal role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. LiDAR being one of the popular perceptual sensor, suffers with large number of inaccurate object detections. This work is an extension of an ongoing research on multiple object detection and tracking. Where, Neural Network based approach is considered for visual detection to aid the LiDAR point cloud processing, and to address the inherent shortcoming of the sensor. It is inferred that the proposed framework would perform in real-time on an embedded platform. In addition, the separate processing of visual and LiDAR sensor data will enable switching to a light weight LiDAR only setup in runtime when required.
PMBLDC Motor Based Ceiling Fan Using an Isolated PFC Zeta Converter

This paper presents an isolated power factor corrected (PFC) zeta converter fed permanent magnet brushless DC motor (PMBLDC) rated at 110V for ceiling fan application. Power factor corrected isolated zeta converter is designed and simulated in discontinuous mode (DCM) for controlling the voltage of DC link with single voltage sensor and inherent power factor correction (PFC) at the input AC supply. A PMBLDC motor is fed with an AC-DC converter cascaded with voltage source inverter. An isolated front end converter deals with power quality improvement (PQI) and reduction in power consumption of PMBLDC motor for low power application. In addition, to minimize the losses in switches, fundamental switching frequency is used for the VSI. The position Hall sensors are used to achieve the electronic commutation in the PMBLDC motor. To control the speed of PMBLDC motor, DC link voltage of AC-DC converter is varied up to 110V. The performance of proposed PMBLDC motor ceiling fan is simulated to confirm the design and to show the satisfactory performance at unity power factor.
Data-driven observer-based model-free adaptive discrete-time terminal sliding mode control of rigid robot manipulators

This paper aims to design a data-driven observer-based model-free adaptive terminal sliding mode controller for rigid robot manipulators whose models are unknown in advance. First, the nonlinear robot manipulator dynamics are transformed to an equivalent linear discrete-time data-model using full-form dynamic linearization technique and multi-observers along with an adaptation law are designed in order to estimate system outputs and pseudo-partitioned Jacobian matrix, respectively. Second, based on a nonlinear data-driven terminal sliding surface, the robust discrete-time controller is obtained. Mathematical analysis guarantees the finite-time convergence and the stability of the closed-loop system. The comparative simulations lighten the superiority of the proposed work and the robustness of the proposed controller against external disturbances is validated by means of simulations.