bluuebunny/crossref_metadata_2025_split huggingface dataset:
Decorin-mediated suppression of tumorigenesis, invasion, and metastasis in inflammatory breast cancer
Authors: Hu Xiaoding , Villodre Emilly S. , Larson Richard , Rahal Omar M. , Wang Xiaoping , Gong Yun , Song Juhee , Krishnamurthy Savitri , Ueno Naoto T. , Tripathy Debu , Woodward Wendy A. , Debeb Bisrat G. 
Published: January 2021

AbstractInflammatory breast cancer (IBC) is a clinically distinct and highly aggressive form of breast cancer with rapid onset and a strong propensity to metastasize. The molecular mechanisms underlying the aggressiveness and metastatic propensity of IBC are largely unknown. Herein, we report that decorin (DCN), a small leucine-rich extracellular matrix proteoglycan, is downregulated in tumors from patients with IBC. Overexpression of DCN in IBC cells markedly decreased migration, invasion, and cancer stem cells in vitro and inhibited tumor growth and metastasis in IBC xenograft mouse models. Mechanistically, DCN functioned as a suppressor of invasion and tumor growth in IBC by destabilizing E-cadherin and inhibiting EGFR/ERK signaling. DCN physically binds E-cadherin in IBC cells and accelerates its degradation through an autophagy-linked lysosomal pathway. We established that DCN inhibits tumorigenesis and metastasis in IBC cells by negatively regulating the E-cadherin/EGFR/ERK axis. Our findings offer a potential therapeutic strategy for IBC, and provide a novel mechanism for IBC pathobiology.
Nanostructured manganese oxides as highly active catalysts for enhanced hydrolysis of bis(4-nitrophenyl)phosphate and catalytic decomposition of methanol
Authors: Šťastný Martin , Issa Gloria , Popelková Daniela , Ederer Jakub , Kormunda Martin , Kříženecká Sylvie , Henych Jiří 
Published: January 2021

The nanostructured manganese oxides (MnOx) exhibited high catalytic activities for hydrolysis of phosphate diester-based substrate bis(4-nitrophenyl)phosphate and decomposition of methanol to carbon monoxide and hydrogen as a potential alternative fuel.
DVC‐Net: A deep neural network model for dense video captioning
Authors: Lee Sujin , Kim Incheol 
Published: January 2021

AbstractDense video captioning (DVC) detects multiple events in an input video and generates natural language sentences to describe each event. Previous studies predominantly used convolutional neural networks to extract visual features from videos but failed to employ high‐level semantics to effectively explain video content such as people, objects, actions, and places, and utilized only limited context information in generating natural language. To overcome these deficiencies, DVC‐Net is proposed, a new deep neural network model that uses high‐level semantics to efficiently represent important events as well as visual features. Additionally, DVC‐Net uses a bidirectional long short‐term memory network, a type of recurrent neural network, to detect events over time. Furthermore, DVC‐Net applies an attention mechanism and context gating to effectively exploit context information in a caption generation step. In experiments conducted versus state‐of‐the‐art models, DVC‐Net presented relative gains of over 1.72% (BLEU@1 score increases from 12.22 to 13.94) and 3.19% (CIDEr score increases from 12.61 to 15.80) on the large‐scale benchmark datasets, namely ActivityNet Captions and MSR‐VTT, respectively.
A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data
Authors: Zilberstein Nicolás , Maya Juan Augusto , Altieri Andrés 
Published: January 2021

AbstractAn approach based on the Green function and the Born approximation is used for impulsive radio ultra‐wideband microwave imaging, in which a permittivity map of the illuminated scenario is estimated using the scattered fields measured at several positions. Two algorithms are applied to this model and compared: the first one solves the inversion problem using a linear operator. The second one is based on the Bayesian compressive sensing technique, where the sparseness of the contrast function is introduced as a priori knowledge in order to improve the inverse mapping. In order to compare both methods, measurements in real scenarios are taken using an ultra‐wideband radar prototype. The results with real measurements illustrate that, for the considered scenarios, the Bayesian compressive sensing imaging algorithm has a better performance in terms of range and cross‐range resolution allowing object detection and shape reconstruction, with a reduced computational burden, and fewer space and frequency measurements, as compared to the linear operator.
Miniaturized inverted ultra‐wideband multiple‐input multiple‐output antenna with high isolation
Authors: Nan Jing‐Chang , Wang Ming‐Huan , Gao Ming‐Ming , Liu Jing 
Published: January 2021

AbstractFor the ultra‐wideband (UWB) transmission requirements in wireless communication systems and better miniaturization of mobile terminal antennas, a compact UWB multiple‐input multiple‐output (MIMO) antenna with higher isolation between units is proposed. The MIMO antenna consists of two semi‐circular antenna units and a defective ground structure. By improving the structure of the ground plane, the antenna units are placed upside down by taking advantage of the inherent directional radiation characteristics of the antenna units, thereby achieving higher isolation. The proposed antenna size is 15 × 22 × 1.6 mm3. The measurement results show that the proposed UWB MIMO antenna satisfies the port reflection coefficient S11 < −10 dB in the 3.4–12.9 GHz band, and the isolation is all greater than 25 dB, which has high isolation. The allowable limit of the envelope correlation coefficient ensures good MIMO performance, which can meet the requirements of UWB wireless communication in practical engineering applications.
Effect of the gate structure on the kink phenomenon in of AlGaN/GaN HEMT
Authors: Osmanoglu Sinan , Ozbay Ekmel 
Published: January 2021

AbstractFor the first time, the effect of the gate structure on the kink phenomenon in of the AlGaN/GaN HEMT is investigated in this study. To provide critical understanding into the kink effect, the kink effect in of the AlGaN/GaN HEMTs is investigated with transistors that have various gate lengths () and gate connected field plate lengths (). The HEMTs are fabricated and characterised at the same conditions, and the equivalent circuit models are used to get consistent results. The experimental results show that the gate structure can play an important role on kink effect in . The results present valuable information on the development of the AlGaN/GaN HEMT technology and the MMIC design regarding kink effect.
A robust tracking algorithm for a human‐following mobile robot
Authors: Tsai Tsung‐Han , Yao Chia‐Hsiang 
Published: January 2021

AbstractThe capability of recognizing and tracking a specific human is considered a key technique for serving mobile robots. This paper proposes a real‐time tracker that uses a stereo camera to track specific people in different environments. A tracker has been designed to detect the selected target from multiple people by use of block matching algorithm, human detection, and colour histogram comparison. Considering the similar colour of the objects, the predictor of the Kalman filter determines the position of the object. Finally, the mobile robot is moved according to the tracking results with a relative distance between the robot and the target. The effectiveness of the proposed method is demonstrated by experiments on many video sequences and real environments.
Level set method with Retinex‐corrected saliency embedded for image segmentation
Authors: Liu Dongmei , Chang Faliang , Zhang Huaxiang , Liu Li 
Published: January 2021

AbstractIt can be a very challenging task when using level set method segmenting natural images with high intensity inhomogeneity and complex background scenes. A new synthesis level set method for robust image segmentation based on the combination of Retinex‐corrected saliency region information and edge information is proposed in this work. First, the Retinex theory is introduced to correct the saliency information extraction. Second, the Retinex‐corrected saliency information is embedded into the level set method due to its advantageous quality which makes a foreground object stand out relative to the backgrounds. Combined with the edge information, the boundary of segmentation will be more precise and smooth. Experiments indicate that the proposed segmentation algorithm is efficient, fast, reliable, and robust.
Chain‐routing scheme with compressive sensing‐based data acquisition for Internet of Things‐based wireless sensor networks
Authors: Aziz Ahmed , Osamy Walid , Khedr Ahmed M. , Salim Ahmed 
Published: January 2021

AbstractThe emerging Internet of Things (IoT)‐based systems that integrate diverse types of sensors, mobiles and other technologies to physical world is becoming increasingly popular for use in wide varieties of applications. Compressive sensing (CS)‐based information acquisition and in‐network compression provide an effective method for accurate data recovery at the base station (BS) with reduced cost of communication. In this study, how CS can be combined with routing protocols for gathering data in IoT‐based Wireless Sensor Networks (WSNs) in an energy‐efficient manner are examined. A novel chain‐routing scheme with CS based data acquisition is introduced that includes the following new algorithms: (1) Seed Estimation Algorithm (SEA) to find the best measurement matrix by selecting the best‐estimated seed, (2) Chain Construction Algorithm (CCA) to organise the network nodes during transmitting and receiving process, (3) Compression approach with reduced consumption of energy that improves the lifetime of the network by minimising the local data traffic, and (4) Reconstruction Algorithm (RA) that reconstructs the original data with minimum reconstruction error. Here, extensive simulation and analysis results prove the performance of our proposed method in improving the network lifetime 35% better than the ECST algorithm and 93% better than the PEGASIS algorithm. In addition, the proposed reconstruction algorithm exceeds the other reconstruction algorithms performance.
IoTHunter: IoT network traffic classification using device specific keywords
Authors: Khandait Pratibha , Hubballi Neminath , Mazumdar Bodhisatwa 
Published: January 2021

AbstractWith the proliferation of IoT devices, network management and security monitoring are becoming a challenge. For the timely detection of IoT device status and their behaviour, traffic classification methods are used. Herein, IoTHunter, a Deep Packet Inspection based IoT traffic classifier, is described. It extracts unique keywords comprising domain names, device names etc. to identify flows belonging to a particular device. IoTHunter automates the keyword extraction using the frequency of occurrence of words belonging to flows of different devices. To further enhance the performance, IoTHunter combines device specific keywords with MAC address of device for subsequent flow labelling. A publicly available IoT dataset is experimented and a good classification accuracy of it over a range of IoT devices is demonstrated.
Design and analysis of photonic crystal ring resonator based 6 × 6 wavelength router for photonic integrated circuits
Authors: Thirumaran Sridarshini , Dhanabalan Shanmuga Sundar , Sannasi Indira Gandhi 
Published: January 2021

AbstractA photonic crystal ring resonator‐based 6 × 6 router has been designed and reported. The router is designed using silicon pillars with a refractive index of 3.47 perforated in the air background of refractive index 1 in a square lattice with a lattice constant of a = 562 nm. The router is designed to operate in the third optical window wavelength which has low loss and most widely used in optical communication systems and networks. Plane‐wave expansion and finite difference time domain method has been used to obtain the bandgap and performance of the designed router, which exhibits acceptable performance such as low insertion loss, low propagation delay, and low crosstalk. These routers will find applications in the photonic integrated circuit that paves a path to all‐optical networks.
Model reference adaptive system based sensorless vector control scheme for a 36‐pulse ac–dc converter fed 9‐level cascaded H‐bridge‐inverter driven induction motor drive
Authors: Kant Piyush , Singh Bhim 
Published: January 2021

AbstractA new modular transformer based 36‐pulse ac–dc converter for a cascaded H‐bridge‐inverter fed medium voltage induction motor drive is proposed in this work. The conventional multi‐pulse ac–dc converter has only 18‐pulses in its line input current with six output dc voltages, whereas, the proposed multi‐pulse ac–dc converter has 36‐pulses in its line current with six output voltages. Due to this, the proposed medium voltage induction motor drive has improved power quality at input end. A 9‐level cascaded H‐bridge‐inverter is applied on drive side to boost the performance of medium voltage induction motor drive. A modified model reference adaptive system based speed sensorless vector control is presented in this paper. A nearest level modulation scheme is used to operate a 9‐level inverter at fundamental frequency switching over the entire speed range of an induction motor drive. The performance of this drive is verified through both simulation and experimental results.
Robust predictive current control of PWM rectifier under unbalanced and distorted network
Authors: Zhang Yongchang , Liu Xiang , Li Bingyu , Liu Jie 
Published: January 2021

AbstractModel predictive current control has been proposed for the control of a pulse width modulation (PWM) rectifier due to its simple principle, and quick response. Model predictive current control achieves good performance for ideal grid voltages when an accurate inductance value is known. However, the actual grid voltages are usually unbalanced, and distorted, and the inductance used in the controller may be different from the real value due to saturation, temperature, and so on. In these conditions, the performance of a PWM rectifier deteriorates significantly due to the current harmonics, and power ripples. To cope with distorted grid voltages and inductance variations, this paper proposes a robust predictive current control for a PWM rectifier, which can achieve sinusoidal grid currents, and a constant active power even under an unbalanced and distorted network. The robustness against an inductance variation is achieved by using an extended state observer based on an ultra‐local model of the system. The current reference is calculated based on the principle of active power ripple elimination and then is tracked by predictive control. The presented experimental results prove that the proposed robust predictive current control scheme achieves good performance under a distorted network even with an inaccurate inductance value.
Unit commitment in a hybrid diesel/wind/pumped‐storage isolated power system considering the net demand intra‐hourly variability
Authors: Fernández‐Muñoz Daniel , Pérez‐Díaz Juan I. 
Published: January 2021

AbstractThis paper presents a novel two‐stage stochastic mixed‐integer linear programming model for the generation scheduling of a hybrid diesel/wind/pumped‐storage power system that considers the intra‐hourly net demand variability during the first stage. The model aims to minimise the scheduling cost, calculates the power system storage opportunity cost and considers that the start‐up cost of thermal units depends on the time passed since the previous shutdown in a detailed manner for the first stage (i.e. using integer variables) and in an approximate manner (relaxing the integrality constraints) for the second stage. To increase the model's computational efficiency, a solution approach based on the Benders decomposition technique is applied. The model is implemented in the real power system of El Hierro island, an isolated hybrid diesel/wind/pumped‐storage power system with a closed‐loop pumped‐storage plant and high wind power installed capacity. The schedules provided by the proposed model are compared to the ones provided by a similar model that does not consider the intra‐hourly net demand variability, resulting in generation schedules with a significantly lower wind energy curtailment. The results demonstrate the importance of considering the intra‐hourly net demand variability in order to integrate more wind energy at a moderate extra cost.
Earth fault distance estimation using travelling waves provided with triacs‐based reclosing in distribution networks
Authors: Elkalashy Nagy I. , Eldeeb Hazem K. , Kawady Tamer A. , Tarhuni Naser G. , Lehtonen Matti , Taalab Abdel‐Maksoud I. , Elsadd Mahmoud A. 
Published: January 2021

AbstractThis study presents an earth fault distance determination algorithm for distribution networks using active travelling waves. Three triacs are used in parallel with a three‐phase breaker poles to overcome the mechanical inequality of the poles’ reclosing times, so that the three phases are simultaneously reclosed. As the proposed fault location technique is an active type with controllable reclosing instant, the arrival time of the reflected surge from the fault point should be stamped precisely. For this purpose, three different travelling‐wave detection algorithms are evaluated including the discrete wavelet transform, Hilbert transform, and signal derivative. The fault location performance is evaluated under different fault conditions such as fault distances, fault resistances, and busbar faults. Due to utilising the reclosing transients, the proposed fault location function successfully estimates the fault distance for different earthing concepts such as unearthed, compensated, and earthed networks. This study is accomplished via simulating a typical 20 kV distribution network by the ATP/EMTP program. The results ensure the superior performance of the proposed fault distance estimation algorithm for earth faults in distribution networks.
Sizing HESS as inertial and primary frequency reserve in low inertia power system
Authors: Akram Umer , Mithulananthan N. , Shah Rakibuzzaman , Pourmousavi S. Ali 
Published: January 2021

AbstractEnergy storage systems are recognised as the potential solution to alleviate the impacts of reduced inertia and intermittency in power systems due to the integration of renewable energy sources. Several energy storage technologies are available in the market with diverse power and energy characteristics, operational limitations, and costs. Besides, frequency regulations in power systems have different requirements, for example, inertial response requires high power for a short period while primary frequency regulation requires steady power for a longer time. Thus, it is crucial to find out the optimum sizes and types of storage technologies for these services. In this paper, a methodology for sizing fast responsive energy storage technologies for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation is developed. The sizing of storage systems for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation is done separately. The sizing of storage for inertial response is done in two steps. A region reduction iterative algorithm is proposed to estimate the storage size for inertial response. The sizing of the storage system for primary frequency regulation is done analytically. The sizing methodology incorporates the frequency dynamics of storage, converters, and other associated controls that affect the frequency response. Moreover, an economic analysis is carried out to find the optimum combination of storage technologies for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation services. The accuracy of the proposed sizing method has been compared with the metaheuristic algorithm based technique. The effectiveness of the proposed method is also compared with those in the literature. Simulation results show that the proposed method outperforms the existing methods in the literature. Finally, the non‐linear simulations revealed the validity of the optimal solutions.
Theoretical and experimental research to development of water‐film cooling system for commercial photovoltaic modules
Authors: da Silva Vinícius Oliveira , Martinez‐Bolanos Julio Romel , Heideier Raphael Bertrand , Gimenes André Luiz Veiga , Udaeta Miguel Edgar Morales , Saidel Marco Antônio 
Published: January 2021

AbstractThis work aims at developing and validating mathematical models and empirically evaluating a water‐film cooling system for commercial photovoltaic modules. Methodologically, thermal, electrical and climatological data measured in a specific purpose on‐grid outdoor test unit were used. In this work they are used to study the improvement in the performance of photovoltaic energy production due to temperature reduction. The first‐order state‐space linear parametric model presents the best performance to predict the temperature of the uncooled photovoltaic modules, with normalised mean square error (NRMSE) of 86.75%. In contrast, the non‐linear model performance of cooled photovoltaic modules is lower, with NRMSE of 44.5%; however, the results show that the performance of the complete thermoelectric model is satisfactory, with NRMSE of 76.38%. On the other hand, empirical tests showed that the cooling system reduces temperature in 15–19%, on average, and to a maximum of 35%. In terms of power, there are average gains of 5–9%, and maximum gains of 12%. Regarding gross generation, there are average gains of 2.3–6%, and maximum gains of 12%. It was concluded that it is possible to mathematically model and predict this generation using non‐linear models with a 0.2% error between modelled and measured generation.
Failure rate and downtime survey of wind turbines located in Spain
Authors: Artigao Estefania , Martin‐Martinez Sergio , Ceña Alberto , Honrubia‐Escribano Andres , Gomez‐Lazaro Emilio 
Published: January 2021

AbstractPublicly available wind turbine failure rate and downtime studies are scarce in the scientific literature. Recent surveys in the field reveal that only 20 studies of wind farms based in Europe, Asia and the U.S. have been published, but there are no studies analysing wind farms located in Spain. With Spain holding the second place in terms of installed capacity in Europe, and the fifth worldwide, the importance of the Spanish wind power sector is evident. The present study comprehensively analyses the Spanish WT fleet in terms of yearly installed capacity and number of machines installed per technology and capacity ranges. The current fleet age ranges are also analysed. Under this scenario, the present work aims to contribute to the literature with a new failure rate and downtime study, where 75 WTs from six wind farms located in Spain are analysed over a period of 11 years. The most critical assemblies are identified per WT technology type and in general for the whole data set. Finally, the failure rate and downtime results obtained for the Spanish survey are compared against a selection of similar previously published studies.