Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Several ZnO-Based Dye-Sensitized Solar Cells With Identical Photoelectrodes, Electrolyte, and Sensitizer

In this work, we have tried to understand the variation of experimentally obtained photovoltaic characteristics (open-circuit voltage (VOC), short-circuit current density (JSC), fill factor (FF), and conversion efficiency (η)) for multiple dye sensitized solar cells (DSSCs) prepared and characterized under identical experimental conditions. Twenty (20) DSSCs are prepared using ZnO photo-anodes, Pt cathodes, I−/I3− electrolyte, N719 sensitizers, and fluorinated tin oxide-based transparent conducting oxide substrates. ZnO is synthesized wet chemically and characterized in terms of its phase, morphology, thermal stability, optical behavior for the preparation of photoanodes. The photovoltaic characteristics are estimated under the exposure of simulated solar light (power of incident light 100 mW cm−2 from air mass 1.5 G). Relative standard deviation values are estimated for each photovoltaic parameter and the trends of deviations are studied for the developed cells. Finally, the charge transfer dynamics of cells having maximum and minimum photoconversion efficiencies are investigated through electrochemical impedance spectroscopy.
Machine Learning Approaches Used for Weather Attributes Forecasting

In the not so distant future, weather is the state of the environment; and it forecasts the state of the atmosphere. Weather prediction is a difficult challenge since the environment is an unbroken, multi-dimensional process. Satellites, Doppler radar, radio signals, aircraft and earth senses gain explicitly environment conditions. Different factual and AI methods are essential for gathering knowledge. These strategies can blend an exceptionally complex mechanized numerical model with the moderately simple perception of the sky. Weather determination is also a difficult problem, as the weather is unpredictable and turbulent. Indeed, even though the system for weather guidance is in the present techniques, weather consistency can in any case be ignored and these frameworks are therefore still to be improved. The goal of the study is to explore how AI can help us improve the estimation of climatic boundaries. In this paper we discuss the different strategies used by driving experts, enforce challenges and present our perspectives on improving the successful system for weather control.
NewsLink: Empowering Intuitive News Search with Knowledge Graphs

News search tools help end users to identify relevant news stories. However, existing search approaches often carry out in a "black-box" process. There is little intuition that helps users understand how the results are related to the query. In this paper, we propose a novel news search framework, called NEWSLINK, to empower intuitive news search by using relationship paths discovered from open Knowledge Graphs (KGs). Specifically, NEWSLINK embeds both a query and news documents to subgraphs, called subgraph embeddings, in the KG. Their embeddings’ overlap induces relationship paths between the involving entities. Two major advantages are obtained by incorporating subgraph embeddings into search. First, they enrich the search context, leading to robust results. Second, the relationship paths linking entities inter and intra news documents can help users better understand and digest the results for the given query. Through both human and automatic evaluations, we verify that NEWSLINK can help users understand the result-to-query relatedness, while its search quality is robust and outperforms many established search approaches, including Apache Lucene and a KG-powered query expansion approach, as well as popular deep learning models, Sentence-BERT (SBERT) and DOC2VEC.
Investigation of Endurance Behavior on HfZrO-Based Charge-Trapping FinFET Devices by Random Telegraph Noise and Subthreshold Swing Techniques

The HfZrO-based charge-trapping (CT) FinFET (CT-FinFET) devices with CT/de-trapping behavior occurring in the gate-stack provide a possible solution for embedded 1T DRAM application. In this brief, a novel method combining the random telegraph noise (RTN) and subthreshold swing (SS) is developed to investigate the endurance behavior of CT-FinFET devices, and the following advances have been achieved: 1) the traps in the gate-stack have been classified into two types: slow traps inside the HfZrO layer with deep energy level and fast traps at the interface with shallow energy level; 2) as the endurance cycles went on, the depth and energy level of slow traps increased, while the depth and energy level of fast traps kept the same, which indicated that the ${V}_{\text {th}}$ shift is caused by the accumulation of holes’ trapping in slow traps inside the HfZrO layer; 3) traps inside the HfZrO layer with deeper energy level and shallower depth can be easily occupied by holes during cycling; and 4) the generation of shallow-level traps at the interface contributes to the memory window (MW) degradation. This provides a useful tool for advancing the CT-FinFET technology.
Numerical and Experimental Analysis of Yb:YAG Thin Disk Regenerative Amplifier

We present an numerical and experimental analysis of a Yb:YAG thin disk regenerative amplifier. Group velocity dispersion, third order dispersion, and self-phase modulation (SPM) effects are considered in the simulations of the amplification process. By virtue of the simulations, a compact femtosecond Yb:YAG thin-disk chirped-pulse regenerative amplifier delivering an average power of 50 W at a central wavelength of 1030 nm with a propagation factor of M2 < 1.4 at a repetition rate of 200 kHz and a pulse duration of 500 fs was constructed. Numerical simulation based on this Yb:YAG thin-disk was carried out to investigate the pulse parameters evolution in each round trip. The measured experimental results in pulse width, spectrum, and pulse energy together fitted quite well with the numerical simulation. This plays an important role in the design of high-peak-power regenerative amplifier and will facilitate further power scaling and suppression of gain narrowing of the whole system.
A Neural Network Method for Nonconvex Optimization and its Application on Parameter Retrieval

Parameter retrieval is a typical nonconvex optimization problem in a wide range of research and engineering fields. Classic methods tackle the parameter retrieval problem by feature extraction from the subspace or transform domain. In this paper, we proposed a network-based method to directly solve the nonconvex optimization problem on parameters estimation of complex exponential signals, with no requirement of labeled data. The proposed network has an architecture similar to the Autoencoder network but with the decoder sub-network replaced by a complex exponential signal generator. After training the network to fit the signal parameters to the acquired data, one could obtain the parameters, i.e., frequencies, decay rates, and intensities, and reconstruct the signal. By this work, we show that with a simple application of a lightweight neural network, nonconvex optimization problems like parameter retrieval can be solved efficiently, even without any intricately designed algorithms. We also discuss the robustness of the network-based method by repeated experiments and present the failure cases to indicate the limitations of this method.
Multi-Objective Application-Driven Approximate Design Method

Approximate Computing (AxC) paradigm aims at designing computing systems that can satisfy the rising performance demands and improve the energy efficiency. AxC exploits the gap between the level of accuracy required by the users, and the actual precision provided by the computing system, for achieving diverse optimizations. Various AxC techniques have been proposed so far in the literature at different abstraction levels from hardware to software. These techniques have been successfully utilized and combined to realize approximate implementations of applications in various domains (e.g. data analytic, scientific computing, multimedia and signal processing, and machine learning). Unfortunately, state-of-the-art approximation methodologies focus on a single abstraction level, such as combining elementary components (e.g., arithmetic operations) which are firstly approximated using component-level metrics and then combined to provide a good trade-off between efficiency and accuracy at the application level. This hinders the possibility for designers to explore different approximation opportunities, optimized for different applications and implementation targets. Therefore, we designed and implemented E- $\mathbb {IDEA}$ , an automatic framework that provides an application-driven approximation approach to find the best approximate versions of a given application targeting different implementations (i.e., hardware and software). E- $\mathbb {IDEA}$ compounds 1) a source-to-source manipulation tool and 2) an evolutionary search engine to automatically realize approximate application variants and perform a Design-Space Exploration (DSE). The latter results in a set of non-dominate approximate solutions in terms of trade-off between accuracy and efficiency. Experimental results validate the effectiveness and the flexibility of the approach in generating optimized approximate implementations of different applications, by using different approximation techniques and different accuracy/error metrics and for different implementation targets.
PREPARATION OF SUPERABSORBENT POLYMERS BY THE INVERSE SUSPENSION METHOD

The superabsorbent polymers based on acrylic acid have been prepared in the presence of ammonium persulfate (APS) as initiator and N,N-methylene- bis- acrylamide (MBA) as crosslinking agent. The suspension polymerisation occurred in aqueous drops dispersed in toluene and stabilised by sorbitan monooleate. Some effects on the swelling degree and size particles such as concentrations of MBA and initiator, neutralization degree (Dn) were studied. Swelling degree 405g/g, reach maximum at 85C and 0.09% crosslinking agent, 1.0% initiator, 75% degree of neutralization, 350 rpm agitation speed was observed.
A non-iterative hybrid Dynamic State Estimation scheme utilizing PMU and SCADA measurements

In this paper, a novel hybrid dynamic state estimation technique (NHDSE) has been proposed for estimating power system states which are primarily bus voltage magnitudes and phase angles at each bus. The proposed NHDSE utilizes a suitable linear relationship existing between measurement and states. Thus, the filtering purpose has been resolved through dynamic state estimation (DSE) approach based on linear Kalman filter rather than popularly used Extended Kalman filter (EKF). With the utilization of linear scheme, the proposed approach is iterative free and thus the observation matrix is calculated only once at the beginning. The proposed scheme is compared with conventional EKF based DSE. In order to corroborate the efficacy of the proposed approach, the same has been applied on two IEEE systems i.e. IEEE 14 and 57 bus test system. Obtained results clearly show the efficacy of the proposed scheme.
An Effective Joint Prediction Model for Travel Demands and Traffic Flows

In this paper, we study how to jointly predict travel demands and traffic flows for all regions of a city at a future time interval. From an empirical analysis of traffic data, we outline three desired properties, namely region-level correlations, temporal periodicity and inter-traffic correlations. Then, we propose a comprehensive neural network based traffic prediction model, where various effective embeddings or encodings are designed to capture the aforementioned properties. First, we design effective region embeddings to capture two forms of region-level correlations: spatially close regions have similar embeddings, and regions with similar properties (e.g., the number of POIs and the number of roads in a region) other than locations have similar embeddings. Second, we extract the "day-in-week" and "time-in-day" and utilize the temporal periodicity in designing the embeddings for time intervals. Third, we propose an effective encoding for past traffic data which captures two forms of inter-traffic correlations - the correlation between past and future traffic, and the correlation between travel demands and traffic flows within past traffic data. Extensive experiments on two real datasets verify the high effectiveness of our model.
Frequency Retrieval from PMU Data Corrupted with Pseudo-Oscillations during Off-Nominal Operation

Phasor measurement units are widely used for enhancing awareness of the operation of transmission and distribution networks. Their synchronized measurements enable the development of multiple near-real-time applications that are unattainable with SCADA data only. However, the accuracy of PMU measurements is highly dependent on the chosen algorithms. This paper illustrates that a certain PMU model is unable to accurately estimate system frequency as it drifts away from its nominal value (the estimated frequency profile is contaminated with pseudo-oscillations). Moreover, three devices connected at the same location produce divergent estimates during off-nominal frequency operation. Then, a technique is proposed for directly estimating frequency from phase angle measurements through numerical derivatives. It is shown to accurately estimate the system frequency regardless of nominal or off-nominal frequency operation, and it eliminates the two issues presented previously.
Multi-Modal Multi-Stream UNET Model for Liver Segmentation

Computer segmentation of abdominal organs using CT and MRI images can benefit diagnosis, treatment, and workload management. In recent years, UNETs have been widely used in medical image segmentation for their precise accuracy. Most of the UNETs current solutions rely on the use of single data modality. Recently, it has been shown that learning from more than one modality at a time can significantly enhance the segmentation accuracy, however most of available multi-modal datasets are not large enough for training complex architectures. In this paper, we worked on a small dataset and proposed a multi-modal dual-stream UNET architecture that learns from unpaired MRI and CT image modalities to improve the segmentation accuracy on each individual one. We tested the practicality of the proposed architecture on Task 1 of the CHAOS segmentation challenge. Results showed that multi-modal/multi-stream learning improved accuracy over single modality learning and that using UNET in the dual stream was superior than using a standard FCN. A “Dice” score of 96.78 was achieved on CT images. To the best of our knowledge, this is one of the highest reported scores yet.
Digestibility, feeding value and limiting amino acids in high-fibre and fibre-reduced sunflower cakes fed to tilapia (Oreochromis niloticus)

Four experiments were conducted at the University of Nairobi, in Kenya, to determine the effect of reducing the amount of fibre in sunflower cake on nutrient digestibility and feed utilization in tilapia (O. niloticus), and to compare this low-fibre cake with a commercially available high-fibre sunflower cake. The extent to which protein from a high-fibre and a fibre-reduced sunflower cake could replace fishmeal protein in tilapia diets, and the effects of supplementing diets made from a low-fibre sunflower cake with amino acids lysine, methionine, and threonine on growth, feed intake, and feed utilization were also investigated. Also of interest was to compare digestibility and feeding value of Kenyan omena fishmeal with that of Low-Temperature (LT) anchovy fishmeal. Tilapia (O. niloticus) fingerlings were used in all the experiments. Water temperatures and dissolved oxygen concentrations were maintained above 26 °C and 5.5 mg/litre respectively. Dehulling of sunflower seeds was done using a manual dehuller. Crude fibre levels in the dehulled cakes were all below 15% (DM basis). Protein from the low-fibre and high-fibre sunflower cakes was well digested by tilapia. The apparent digestibilities of protein in the sunflower cakes and the fishmeals were not significantly different. Reduction of fibre in sunflower cake had no effect on the digestibility of protein. Digestibility of energy in the sunflower cakes was low. Apparent digestibility coefficient for energy (ADC-E) and digestible energy concentration (DE) were higher in the low-fibre sunflower cake than in the high fibre cake, but the differences were only significant for DE. There were no differences in the apparent digestibilities of protein, energy and organic matter between omena and anchovy fishmeals.
A Controllable Spring Force Based Method for Fluid Surface Disturbance Details Simulation

Particle based simulations are widely used in computer graphics. However, few methods use active control for fluid surface. Thus we present a new controllable method based on the spring force model, which provides active control for the disturbance of the fluid surface. This method modifies the spatial distribution of the particles to generate disturbance. To make it controllable, we design a disturbance equation, using the relationship between the macrostate of fluid and the microscopic particles in the Boltzmann’s Entropic Equation. Moreover, we also propose multiple optimizations in order to improve appearance of fluid surface. The correction of fluid splash first detects the target particles by a hybrid method, then adjusts the spring force that acts on these particles. The spring force attenuation model uses a sigmoid function to represent the attenuation of particles at different depths. Experimental results show that our method generates the expected disturbance details. The optimizations enhance the realism of the disturbance simulation.
The Influence of Minority Carrier Density on Degradation and Regeneration Kinetics in Multicrystalline Silicon Wafers

The rate at which silicon solar cells and wafers degrade and regenerate when subjected to light and elevated temperatures (LeTID) is known to depend on the minority carrier concentration. In this article, we utilize spatial differences in the rate of degradation and regeneration in multicrystalline silicon wafers to estimate the dependence of these rates on minority carrier concentration. We apply the relation obtained from spatial investigations in a model describing temporal LeTID defect evolution. With the updated model in which the evolving minority charge carrier density during degradation and regeneration is considered, we obtain a drastically improved fit between measured and modeled data. The activation energy for the regeneration process determined from our proposed model (0.56±0.12eV at 1 sun) is lower than most values reported in the literature but correspond perfectly to the activation energy reported by Graf et al. by whom evolving carrier injection is accounted for experimentally. Hence, our results underpin the importance of taking the evolution of charge carrier injection into account in the evaluation of LeTID kinetics. Finally, we use our updated model to obtain spatial maps of degradation and regeneration parameters. Clear correlations are shown between LeTID kinetics parameters and local processing conditions.
Guidelines for Ferroelectric-Semiconductor Tunnel Junction Optimization by Band Structure Engineering

Tunneling processes of ferroelectric tunnel junction (FTJ) based on metal-ferroelectric-insulator-semiconductor (MFIS) stack are studied for both n- and p-type semiconductor electrodes using experimentally calibrated model. In the model, the calculation of ON- (<inline-formula> <tex-math notation="LaTeX">${J}_{mathrm {ON}}$ </tex-math></inline-formula>) and OFF-current (<inline-formula> <tex-math notation="LaTeX">${J}_{mathrm {OFF}}$ </tex-math></inline-formula>) in MFIS-FTJ considers the electron tunneling from both conduction (CBE) and valence band (VBE), and hole tunneling from the valence band (VBH). Depending on metal work function, doping concentration and remnant polarization, the conduction modes in both n- and p-type MFIS can respectively be classified according to the contribution of minority carriers in semiconductor. The optimal tunneling electroresistance (TER) (<inline-formula> <tex-math notation="LaTeX">$\approx {J} _{mathrm {ON}}/{J}_{mathrm {OFF}}$ </tex-math></inline-formula>) for the n-type device is achieved, when semiconductor works under depletion/accumulation state driven by polarization reversal. With respect to its p-type counterpart, the accumulation/strong inversion state is preferable. The underlying physics are revealed, that if semiconductor goes into the strong inversion, the tunneling of VBH will increase the <inline-formula> <tex-math notation="LaTeX">${J}_{mathrm {OFF}}$ </tex-math></inline-formula> and decrease the TER ratio in n-type device, whereas the tunneling of CBE will increase the <inline-formula> <tex-math notation="LaTeX">${J}_{mathrm {ON}}$ </tex-math></inline-formula> and thus TER ratio in p-type device. Guided by the model, the band structure engineering of MFIS structure is provided to design and optimization of FTJ performance.
Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)

Bearings play a vital role in all rotating machinery, and their failure is one of the significant causes of machine breakdown leading to a profound loss of safety and property. Therefore, the failure of rolling element bearings should be detected early while the machine fault is small. This paper presents the model that detects bearing failures using the continuous wavelet transform and classifies them using a switchable normalization-based convolutional neural network (SN-CNN). State-of-the-art accuracy was achieved with the proposed model using the Case Western Reserve University (CWRU) bearing dataset, which serves as the primary dataset for validating various algorithms for bearing failure detection. Batch normalization techniques were also employed and compared to the proposed model. The spectrogram images were also used as input for further comparison. Using switchable normalization, the proposed model achieved the testing accuracy in between 99.44% and 100% for different batch sizes and datasets.
Mechanisms of branch tip fusion in meshwork patterns*

Meshwork pattern is a significant pattern for the development of biological tissues and organs. The formation of meshwork pattern is facilitated by branch tip fusion. However, the mechanism of branch tip fusion is still unclear. In this paper, based on a reaction-diffusion model, we found that the local high concentration substrate guided the fusion of branch tips and provided the quantitative ranges of environmental conditions for branch tip fusion. Then we explored the Turing wavelengths underlying branch tip fusions. We found that the width of local high concentration substrate region did not influence the Turing wavelength of branch tip fusion and the width less than two Turing wavelengths promoted the fusion of branches. We also found that the increasing Co (production rate of substrate) reduced the Turing wavelength and potential of branch tip fusion. We believe that the molecular mechanism and Turing mechanism of branch tip fusion in this paper will be beneficial to the in-depth understanding of network morphogenesis.