Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Recognition of Underwater Acoustic Target Noise by Multi-Feature Fusion

The traditional methods of underwater acoustic target recognition need to manually extract the feature data, and the recognition process has strong human-computer interaction characteristics, which couldn't meet the demand of intelligence of underwater acoustic target recognition in the future. In this paper, a target recognition method based on Long Short-term memory network (LSTM) is proposed, which fuses multiple feature data and has a certain degree if intelligence ability. In this method, the Long Short-term memory network (LSTM) is adopted to generate various kinds of underwater acoustic target noise feature data. The SOFTMAX classifier is used to fuse multi-feature and to classify the targets. At last, this paper uses the time domain data, frequency data and Mel cepstrum data as input data to verify the model, the recognition results show that the model which fuse three kinds of input data above is better than the other methods which only one or two kinds of feature data, which meets the intelligent requirements of underwater acoustic target recognition to a certain extent.
Generation of a novel HEK293 luciferase reporter cell line by CRISPR/Cas9-mediated site-specific integration in the genome to explore the transcriptional regulation of the PGRN gene

ABSTRACT Progranulin has multiple functions in several physiological and pathological processes, including embryonic development, wound repair, tumorigenesis, inflammation and neurodegeneration. To investigate the transcriptional regulation of the PGRN gene, a luciferase knock-in reporter system was established in HEK293 cells by integrating luciferase gene in the genome controlled by the endogenous PGRN promoter using CRISPR/Cas9. PCR results demonstrated the site-specific integration of the exogenous luciferase gene into the genome. To validate the novel luciferase knock-in system, a CRISPR/Cas9 transcription activation/repression system for the PGRN gene was constructed and applied to the knock-in system. In addition, phorbol ester (phorbol 12-myristate, 13-acetate), previously reported as activating the expression of PGRN, was applied to the system. The results indicated that luciferase activity was directly correlated with the activity of the PGRN endogenous promoter. This novel system will be a useful tool for investigating the transcriptional regulation of PGRN, and it has great potential in screening the drugs targeting PGRN.
EVENT NETWORKS AND THE SUPERSTAR MODEL By

Condensation phenomenon is often observed in social networks such as Twitter where one “superstar” vertex gains a positive fraction of the edges, while the remaining empirical degree distribution still exhibits a power law tail. We formulate a mathematically tractable model for this phenomenon that provides a better fit to empirical data than the standard preferential attachment model across an array of networks observed in Twitter. Using embeddings in an equivalent continuous time version of the process, and adapting techniques from the stable age-distribution theory of branching processes, we prove limit results for the proportion of edges that condense around the superstar, the degree distribution of the remaining vertices, maximal nonsuperstar degree asymptotics and height of these random trees in the large network limit.
Ectopic Expression of a Fagopyrum esculentum APETALA1 Ortholog only Rescues Sepal Development in Arabidopsis ap1 Mutant

Fagopyrum esculentum (Polygonaceae: Caryophyllales) exhibits an undifferentiated perianth comprising five showy tepals, which does not completely correspond to the perianth differentiated into typical sepals and petals in most core eudicots. In Arabidopsis, the APETALA1 (AP1) gene is involved in specifying sepals and petals development. Here we isolated AP1 ortholog, FaesAP1, and a 2.2kb FaesAP1 promoter (pFaesAP1) from F. esculentum. FaesAP1 expression is mainly detectable in all floral organs and maintains at a high level when tepals elongate rapidly both in pin and thrum flowers. Moreover, the GUS reporter gene driven by pFaesAP1 was activated in flowers where the sepals were intense, but the petals very weak or absent. Additionally, FaesAP1 ectopic expression in Arabidopsis ap1-10 mutant rescues sepal development fully, obviously prompting early flowering, but failing to complement petal development. In this study, evidence was provided that the showy tepals in the F. esculentum are homologs to core eudicots sepals. Furthermore, these findings show a different perianth identity program in Caryophyllales, suggesting that AP1 orthologs involved in petal development may evolve independently across different clades of core eudicots. Our results also suggest that FaesAP1 holds potential for biotechnical engineering to develop early flowering varieties of F. esculentum.
An improved minimum generation error based model adaptation for HMM-based speech synthesis

A minimum generation error (MGE) criterion had been proposed for model training in HMM-based speech synthesis. In this paper, we apply the MGE criterion to model adaptation for HMM-based speech synthesis, and introduce an MGE linear regression (MGELR) based model adaptation algorithm, where the regression matrices used to transform source models are optimized so as to minimize the generation errors of adaptation data. In addition, we incorporate the recent improvements of MGE criterion into MGELR-based model adaptation, including state alignment under MGE criterion and using a log spectral distortion (LSD) instead of Euclidean distance for spectral distortion measure. From the experimental results, the adaptation performance was improved after incorporating these two techniques, and the formal listening tests showed that the quality and speaker similarity of synthesized speech after MGELRbased adaptation were significantly improved over the original MLLR-based adaptation.
Tuning in to Personalized Music: A Spotify API-Based Hybrid Recommendation System Integrating Content-Based and Popularity-Based Approaches

This research paper offers a comprehensive approach for constructing a successful music recommendation system that integrates popularity- and content-based approaches. The suggested method intends to improve music discovery for users in an era of expansive music libraries and personalized user experiences. It makes use of the Spotify API to compile thorough track data and employs data preparation methods to guarantee data quality. The recommendation engine is the center of the research. Song recommendations are personalized by a content-based algorithm that bases them on aural features. In addition, a hybrid model balances user preferences and trending music by weighting track popularity by release date. A user-friendly GUI is included to make it easier for users to interact, allowing users to input their favorite music and get recommendations right away. Although the code serves as the basis for a strong recommendation system, later versions can include performance criteria for evaluation, such as accuracy, precision, and recall. This study contributes to the constantly developing field of music technology and user pleasure by providing a foundation for the improvement of music recommendation systems.
Clutter Suppression and Weak Target Detection Using EEMD-WOA Algorithm for FMCW Radar

Weak target detection poses a significant challenge in FMCW radar systems due to substantial near-range clutter. Conventional MTI algorithms and clutter map methods rely on the intensity and doppler frequency differences between the clutter and the moving target in multiple data frames. However, the suppression performance becomes inadequate when the clutter intensity is substantial and the multipath effect is significant. Additionally, feeble target leakage occurs and target detection is impaired following clutter suppression. This paper presents a joint EEMD-WOA processing framework that addresses the abovementioned issues. The framework eliminates clutter by employing ensemble empirical modal decomposition (EEMD) of multi-frame signals to extract the signal subspace. Additionally, the whale optimization algorithm (WOA) is utilized to extract signals by exploiting the disparity in higher-order statistics between the clutter and the targets, thereby accomplishing the objective of weak target detection. Simulation analysis substantiates the superior detection performance of this method in comparison to alternative methods.
Experiments on Coexistence of Microwave WPT and Energy Harvesting with a Matrix Switching Circuit and Solar Power Cells

DC power supplying is an important problem for the practical use of IoT with massive sensors. There are two techniques for such supplying. One is the wireless power transfer (WPT) technique and the other is energy harvesting. This paper focuses on the combination of WPT and energy harvesting. WPT can transmit higher power to the receiving equipment than that energy harvesters can collect. Reversely, energy harvesting is a more environmentally friendly power supply method than WPT. WPT is an active charging method, whereas energy harvesting is a passive charging method. Herein, the combination of WPT and energy harvesting is a suitable power supply method for IoT. This research is focusing on the i-v characteristics of rectifiers and solar power cells. We experimented the characteristics of a rectifier with differential input power and solar power cells with room light. We also experimented the i-v characteristics of multi-connected rectifier and solar power cells. Our proposing matrix switching circuit is used for the multi connection. This matrix circuit enables any connections for the DC power sources. The experimental results shows both positive and negative effect on the DC power supplying.
3.8×3.8 mm2 Tiny Piezoelectric Resonant MEMS Scanner Using Fork-Shaped and Ring-Shaped Actuators

We developed a 3.8×3.8 mm2 tiny resonant piezoelectric MEMS scanner with a 1.0-mm mirror using double coupling frames. The proposed scanner utilizes the mechanical coupling of two Pb(Zr,Ti)O3 (PZT) piezoelectric actuators: fork-shaped and ring-shaped actuators. The proposed two-actuator design increases the efficiency of area usage for driving actuators per die and improves the resonant frequency owing to the mechanical coupling of the two actuators. Besides, the proposed design reduces mechanical stress by making the operating scanning frequency higher than the other scanning modes. The resonant frequency of the proposed scanner is 27.09 kHz, and the optical scan angle is 40 degrees with a unipolar driving voltage of 25.2 V.
An L-band Array with LCMV Beamforming Using the Xilinx RFSoC

LCMV beamforming for an L-band antenna array using the Xilinx RFSoC is presented. Implementation of the fully digital beamformer is discussed, including details of the implementation in hardware, High Level Synthesis (HLS), and software. The beamformer is demonstrated for several LCMV cases using an L-band array with eight elements.
Benefits and Drawbacks of Harboring Plasmid pP32BP2, Identified in Arctic Psychrophilic Bacterium Psychrobacter sp. DAB_AL32B

Psychrobacter sp. DAB_AL32B, originating from Spitsbergen island (Arctic), carries the large plasmid pP32BP2 (54,438 bp). Analysis of the pP32BP2 nucleotide sequence revealed the presence of three predicted phenotypic modules that comprise nearly 30% of the plasmid genome. These modules appear to be involved in fimbriae synthesis via the chaperone-usher pathway (FIM module) and the aerobic and anaerobic metabolism of carnitine (CAR and CAI modules, respectively). The FIM module was found to be functional in diverse hosts since it facilitated the attachment of bacterial cells to abiotic surfaces, enhancing biofilm formation. The CAI module did not show measurable activity in any of the tested strains. Interestingly, the CAR module enabled the enzymatic breakdown of carnitine, but this led to the formation of the toxic by-product trimethylamine, which inhibited bacterial growth. Thus, on the one hand, pP32BP2 can enhance biofilm formation, a highly advantageous feature in cold environments, while on the other, it may prevent bacterial growth under certain environmental conditions. The detrimental effect of harboring pP32BP2 (and its CAR module) seems to be conditional, since this replicon may also confer the ability to use carnitine as an alternative carbon source, although a pathway to utilize trimethylamine is most probably necessary to make this beneficial. Therefore, the phenotype determined by this CAR-containing plasmid depends on the metabolic background of the host strain.
Qualitative Geometrical Uncertainty in a Topological Robot Localization System

Despite the great advances in robotics made in recent years, there is not yet a universal model that allows a robot to move autonomously, safely and taking into account the most important elements of the surrounding environment. Autonomous and safe navigation requires that the robot knows its location and the location of obstacles and this is achieved through localization. Localization is the process of knowing and updating continuously a robot position with regards to its environment based on sensor information. The aim of this work is to develop a localization system that allows a mobile robot to know its location at each moment taking into account environmental uncertainty. In this paper, uncertainty estimation models are applied to a Topological Navigation algorithm using Hidden Markov Models (HMMs). HMMs offer a robust and efficient solution for the definition of discrete representations. HMMs are proposed as the basis to build an improved probabilistic algorithm using geometric information. Topological localization is used as it scales better to large environments and its navigation strategies are more natural. The main contribution is utilizing Hidden Markov Models and qualitative geometric information to infer robot localization in a topological map taking uncertainty into account. Finally, experiments have been carried out, their results show the feasibility of the localization system and the improvement due to the method proposed.
Banking Industry Using Artificial Neural Network

The study aims to create a method to determine the credit rating and evaluate the risks related to the Indian banking business. To create predictive models for credit ratings, Artificial Neural Network (ANN) algorithms are used, which include Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. For this purpose, secondary data from over 254 Indian banks covering 2014–15 to 2021–22 were considered. The study evaluated crucial factors to predict credit scores while considering business, financial, and industrial hazards. The data includes current liability and Revenues, COGS, SG&A, R&D, EBIT, EBITDA, Interest expense, Abnormal Gains/Losses, Income Taxes, Net Income from Discontinued Op. , Net Profit, and Dividends. Current and quick ratios. GDP per capita, The research's main contribution is its capacity to forecast how credit ratings for different Indian banks may change over time based on their financial, commercial, and industrial risk profiles. This cutting-edge method of credit rating prediction improves forecasting accuracy. It boosts the effectiveness of rating systems, providing invaluable insights for the banking sector to navigate these uncharted times while analysing credit risks successfully.
Deep Learning-Based Equivalent Modelling of Hybrid RES Plant for Efficient, Repetitive Power System Transient Stability Studies

The article presents the methodology for dynamic equivalent modelling of hybrid renewable energy source (HRES) plant with the aim of obtaining highly accurate time domain HRES plant power responses at any time during the year. The focus is on equivalent modelling for transient stability studies. Historical HRES plant production dataset and transmission network short-circuit fault statistical data, along with unsupervised data mining and deep learning techniques, represent a basis of the modelling procedure. Dynamic equivalent model (DEM) is developed in the form of deep Long Short Term Memory network. Real and imaginary parts of voltage at the point of common coupling (PCC) are model inputs, while real and reactive power at the PCC are model outputs. The article also proposes a practical approach for selecting the adequate DEM at any time in a year based on the forecasted HRES plant operating scenario only. Model performance is evaluated on a large number of case studies using the HRES plant consisting of three renewable energy sources. The results have shown that only a few DEMs are required for representing the annual HRES plant dynamic performance and obtaining reliable transient system stability results.
Three Cases Describing Outcomes of Negative-Pressure Wound Therapy With Instillation for Complex Wound Healing

BACKGROUND: We describe 3 cases where negative-pressure wound therapy with instillation and dwell time (NPWTi-d) was used as an adjunctive therapy for 3 chronic wounds. CASES: Three patients (2 males and 1 female), ranging in age from 28 to 53 years, presented with complex, infected wounds: (1) a diabetic foot ulcer with underlying infection, (2) a dehisced abdominal wound with enterocutaneous fistula, and (3) a large wound of the upper torso and axillary region resulting from soft tissue necrosis. Negative-pressure wound therapy with instillation and dwell time was initiated by instilling normal saline or an antiseptic solution; the solution was left in place for 3 to 10 minutes. Continuous negative pressure was then applied at −125 or −150 mm Hg; cycles were repeated every 1 or 3 hours. Treatment was applied for 5 to 44 days, and dressings were changed every 2 to 3 days. Granulation tissue developed in all 3 wounds; all closed after subsequent skin grafting. CONCLUSIONS: Outcomes of these cases suggest that NPWTi-d may be used as an adjunctive treatment modality for a variety of chronic wounds.
A Parametric Study of Deep Perceptual Model on Visible to Thermal Face Recognition

Recently deep perceptual mapping (DPM) based on auto-encoder provides the state-the-art thermal to visible face recognition. Features extracted from patches of a long-wave infra-red (LWIR) face image are transformed into a space by an auto-encoder, such that features from infra-red images are comparable with features from visible images. In this paper, we comprehensively evaluate DPM with different settings, in order to build a reference study for future research.
Fusion of Prior Knowledge and Gated Neural Networks for Short Text Recognition

This paper introduces an innovative approach to short text recognition, namely the Fusion of Text Word Frequency Features with Gated Neural Networks. Due to the limited and low inter-correlation of information in short texts, traditional classification methods perform poorly in handling such texts. To address this issue, we propose a novel model called the PK-BIGRU (Priori Knowledge BIGRU) model. This model leverages TF-IDF calculations of text vocabulary word frequencies to generate text feature vectors with prior knowledge, which are then fused with the context semantic feature vectors extracted by BIGRU from the original text. This innovative approach extends the feature information of short texts, significantly improving the recognition efficiency of the model. In real short text validation and comparative experiments, the PK-BIGRU model demonstrates outstanding performance, achieving the highest precision improvement of 1.9%, the highest recall improvement of 3.3%, and the highest F1 score improvement of 2.4%. These metrics surpass other models in various aspects. The PK-BIGRU model achieves a balance between semantic understanding and frequency-based information capture, exhibiting a clear advantage in handling short text scenarios where traditional methods struggle, by leveraging the characteristics of both types of information.
Ultra-Flexible Multichannel Neural Microelectrodes Modified by PEDOT:PSS/IrOx for Enhanced Sensing and Localized Stimulation

We report an ultra-flexible multi-channel neural electrode with a novel modified approach to achieve the performance of high charge storage capacity. This kind of neural electrodes possess the thickness of ~ 2.5 μm, the width of ~ 100 μm, and bending stiffness of ~3.9×10-13 N•m2, which testify to its outstanding flexibility, higher biocompatibility and working stability. Moreover, ultrathin flexible neural electrode modified with PEDOT: PSS/IrOx has a high charge storage capacity of ~ 24.2 mC/cm2, which is ~ 27.9 times and ~1.6 times of PEDOT:PSS and IrOx alone, presenting superior performance in electrical stimulation. The extremely thin thickness and modified nanomaterials both benefit long-term high-quality neurophysiological signal recording. By implanting the ultraflexible neural electrode, it was verified that the modified electrode had long-term stable signal recording ability for 6 weeks and good localized electrical stimulation effect, which demonstrated to be a powerful tool for the research of brain neuroscience.
Laser-Based Fabrication Process for Piezoresistive Cantilever Using Flash Laser Annealing

This paper reports a simple laser-based fabrication process for a piezoresistive cantilever involving flash laser annealing. The proposed process simultaneously realized a micro cantilever structure and an n-type piezoresistive sensing element by irradiating a dopant-coated Si wafer with a pulsed fiber laser. The fabricated sensors responded linearly to strain with a gauge factor over 10, a sufficiently useful value. The proposed process could serve as a rapid prototyping in MEMS processes involving semiconductor doping.
Unsupervised Person Re-Identification Using Similarity Score

Unsupervised person re-identification is a challenging task in computer vision that aims to match individuals across multiple non-overlapping camera views without the need for annotated training data. In this research, we propose a novel approach for unsupervised person re-identification using similarity scores. Our method leverages the concept of similarity scores to measure the likeness between individuals based on their visual appearance. We employ resnet to extract robust and discriminative features from the person images. These features are then used to compute pairwise similarity scores between all possible image pairs. In conclusion, our unsupervised person re-identification method utilizing similarity scores offers a promising solution for addressing problem to match individuals. The proposed approach demonstrates superior performance and robustness, opening avenues for various applications such as video surveillance, crowd analysis, and person tracking in real-world scenarios.
Detection of Factors Influencing Market Liquidity Using an Agent-Based Simulation

Recently, investors have been becoming more interested in market liquidity. Liquidity is regarded as a measure of a booming financial market; market participants are able to smoothly buy and sell their intended amount at a price close to the market mid-price. When discussing market liquidity in empirical studies, researchers have defined liquidity indicators that are consistent with their research objectives. However, it has not been clarified which market factors affect these indicators. Therefore, we investigated which market factors affect major liquidity indicators by using an artificial market. As a result, the four liquidity indicators Volume, Tightness, Resiliency, and Depth were found to be affected by tick size and investor strategies (fundamental, technical, and noise) among the parameters of the artificial market.