Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low-Ripple Switched-Capacitor-based DC-DC Converter with Optimal Efficiency Tracking for Self-Powered Wearable Devices

This paper presents a low-ripple, switched-capacitor-based DC-DC converter with an optimal efficiency tracking scheme. The proposed converter that interfaces with energy harvesters, such as thermoelectric generators (TEG) and photovoltaic (PV) solar cells, provides a supply voltage with minimized ripples to the sensing units in self-powered wearable health monitoring devices. The proposed converter employs a reconfigurable charge pump (RCP) with 7 conversion ratios (CRs), a CR tunning loop, and a voltage regulation loop (VRL). The CR tunning loop adjusts the CR to its optimal value not only for adapting input voltage changes across a range of 0.35 – 1 V but also for tracking the optimal end-to-end power conversion efficiency (ηEE). Under each CR, the VRL controls the RCP to regulate the output voltage at 2 V. The output ripples are below 1 mV, eliminating the need for an extra regulator. The converter can provide up to 200 μA load current and achieve a peak ηEE of 74.5%. In addition, the converter implemented a cold start charge pump to enable self-sustained operation. The proposed DC-DC converter was fabricated in a 180 nm standard CMOS process and occupied a silicon area of 2.7 mm2.
Graph Representation of Chinese EMRs and its Short-text Classification Application1

Chinese Electrical Medical Records (EMRs) include not only text entities features but also relationship features. However, the remaining representation solutions of Traditional Chinese Electrical Medical Records (EMRs) just focuses on entities and context without other relationships, lacking details and limiting the text analysis performance of NLP models. Therefore, a graph representation solution that combines entities features and relationship features is introduced. Specifically, Point-wised Mutual Information (PMI) is leveraged to represent relationships among text entities, and Word2Vec to represent text entities themselves. As a presentation and evaluation of NLP application, GCN is applied to construct word2vec+GCN text classification solution that classifies short text-data from Chinese EMRs in text classification evaluation of TCM medical text records. As a result, word2vec-GCN achieved 89.51% of average precision, 89.83% of average recall, 89.06% of Macro F1 88.90% of Kappa, and 98.90% AUC values, outperforming other solutions based on ordinary NLP models. Meanwhile, the ablation experiment was introduced to validate the hypotheses that the performance improvement would be caused by the embedding of relationships features, and the hypotheses was approved. It could be concluded that the graph representation based on word2vec and PMI is available, improving the qualification of text representation and performance of NLP tasks like text classification, even if this graph representation is utilized by ordinary NLP models.
About a Possible High Poles Observer Design Instead of High Gain for Triangular Nonlinear Systems

This paper is about observer design for a class of nonlinear systems admitting a possible solution via the well-known high-gain technique. Here instead, a design based on full pole placement for the linear part of the observer is discussed. In particular it is shown that exponential convergence of the observation error is guaranteed in spite of nonlinearities, for poles chosen large enough, with observer gains which can be lower than in a standard high gain approach. A focus is given on systems up to order three, and a discussion on measurement noise effect is provided. Simulation examples are included to illustrate the conclusions.
Configurable Multimodal Therapeutic System for Promoting Emotional Relaxation

This paper presents a study on a configurable multimodal therapeutic system designed to analyze the impact of various therapeutic environments on participants' health and determine more effective approaches. The system aims to relax individuals and gradually enhance their well-being by manipulating the surrounding lighting conditions. The experiment involved 22 adults participating in four rounds, each exploring the effects of colored light, flickering frequencies, feedback regulation, and multimodal stimuli. EEG parameters were measured, and pre- and post-assessment stress surveys were conducted to understand the effects of different therapeutic environments on participants' stress levels. Statistical analysis using one-way analysis of variance (ANOVA) and t-tests revealed significant results. Blue light, non-flickering light, stimuli with feedback regulation, and multimodal stimuli were found to be more effective in promoting emotional relaxation. The findings emphasize the importance of considering different therapeutic environments and highlight the potential benefits of a configurable multimodal therapeutic system for identifying effective approaches.
An Asymmetric Birdcage Coil Design for 19F Lung MR Imaging at 1.5 T

Magnetic resonance imaging (MRI) based on 19F can detect the lung pulmonary ventilation status without radiation and lower cost, which has emerged as a promising approach in the post-COVID era. In this paper, we propose an asymmetric radio frequency (RF) birdcage coil uniquely tailored for 1H-19F lung MR imaging system. This newly designed coil can work nested into the 1H volume coil, thereby enabling simultaneous structural and functional imaging and enhancing patient comfort. After employing conformal mapping to obtain the transformed closed curve, ideal current distributions are directly assigned to finish capacitance calculations, enabling the asymmetric birdcage coil to yield a uniform RF field. The intensity and homogeneity of the produced field are validated by theoretic calculation and electromagnetic simulation. Upon those results, compared to the conventional circular birdcage coil, our novel coil design demonstrates superior performance, establishing its efficacy and superiority.
The smart analysis of cell damage and cancerous prediction using information clustering model

with the assistance of Information Clustering Algorithm, the smart analysis of cellular damage and cancerous prediction is becoming easier and much more accurate over time. This algorithm has the potential to change the way medical research is conducted and its results used to predict cancer, as it produces more accurate and timely data regarding cellular damage on a molecular level. By utilizing this algorithm, researchers are able to detect changes in cellular information that have been correlated with the development of cancerous cells, as well as cellular damage caused by external factors such as radiation exposure and environmental factors. In addition, the algorithm can be used to analyze the molecular structure of existing cells and to detect whether they are at risk of malignant transformation. This allows for more precise diagnosis and treatment of cancer, and also helps to better understand the underlying causes of cancer. Furthermore, this algorithm can help identify new targets for drug therapy and also present promising avenues for further research regarding cancer and its causes.
A Machine Learning Approach for Hydroelectric Power Forecasting

Sustainable energy solutions are essential for a cleaner and greener future, and hydroelectric power responds to this need. Recently, hydroelectric power plants have been integrated into some Water Distribution Systems (WDSs) to harness hydraulic jumps for electricity production. Optimizing the performance of these plants requires accurate forecasting of hydropower generation. Such forecasts are crucial for various purposes, including capacity planning, energy management, decision-making, and anomaly detection. This paper aims to develop a one-step-ahead monthly forecasting model for hydroelectric plants integrated into WDSs using machine learning algorithms not previously utilized in this context. In contrast to existing literature that primarily focuses on general run-of-river or storage-reservoir-based systems, the proposed approach is applicable to all WDSs-integrated plants, whose primary function is water delivery to consumers. After testing more than 500 models, the Temporal Convolutional Network architecture, with specific hyperparameters, achieved the best performance.
Bio-FP: Biochip Fingerprints for Authentication

Microfluidic biochips are widely used in biological computing, clinical diagnostics, and point-of-care tests. However, the growing demand and the complex supply chain of biochips expose them to intellectual property (IP) attacks such as counterfeiting, overbuilding, and piracy. To address this issue, we present a biochip-level fingerprinting (Bio-FP) scheme. We utilize melt-electrospinning printing technique to print unique Bio-FPs directly onto biochips. Then, a layer of polydimethylsiloxane (PDMS) is applied through spin-coating to obscure the Bio-FPs. If the Bio-FPs are doped with a fluorescent dye, they can be detected by shining UV light. Authentication of dyed Bio-FPs is achieved through spectral analysis by mapping the intensitywavelength response. To optimize the authentication scheme for Bio-FPs, several pre-processing techniques were employed to enhance their quality. Additionally, transfer learning and finetuning were utilized with multiple deep learning models, yielding a high Bio-FP classification accuracy of 95.8%.
A PIB-Oriented Elliptic Beam Quality Calculation Based on Energy Distribution

The precise assessment of beam quality is a fundamental element in the design and practical applications of lasers. Conventional techniques relying on optical instruments or image processing, primarily centered on circular spot analysis, encounter inherent limitations. This study addresses the pressing need for an enhanced, user-centric methodology to compute the quality factor accurately, $\beta$. Motivated by the shortcomings of established methods, particularly in dealing with intricate spot shapes and adhering to Power in the Bucket (PIB) requirements, our approach integrates image processing and computer vision for $\beta$ computation. Our method commences with image preprocessing for noise reduction and precise cropping, followed by an orientation gradient to rectify skewness. This is coupled with an integration step for initial beam width estimation, culminating in the derivation of $\beta$ Simultaneously, we introduce an innovative Ellipse Detection algorithm to mitigate spot deformation interference. Additionally, we devise a beamwidth tuning algorithm incorporating a bucket-type data structure to meet PIB requirements swiftly. Compared to traditional techniques, the results exhibit marked superiority in accuracy, computational speed, and user-friendliness. This novel approach presents a promising alternative for laser beamwidth determination, showcasing its potential for widespread adoption in practical applications.
Deep-USIpred: identifying substrates of ubiquitin protein ligases E3 and deubiquitinases with pretrained protein embedding and bayesian neural network

Identifying the substrates of ubiquitin protein ligase (E3) and deubiquitinases (DUB) contributes to the discovery of potential therapeutic targets for diseases. However, experimental identification of E3/DUB-substrate interactions is costly and time-consuming. Current computational methods for predicting E3/DUB-substrate interactions rely heavily on specific domain knowledge and involve complex and diverse biological data processing. To address this challenge, we proposed a deep learning prediction model, named Deep-USIpred, which predicts E3/DUB-substrate interactions using protein sequences. The proposed Deep-USIpred model encodes protein sequences with a pretrained model and utilizes 1DCNN-BNN deep learning algorithm to make a robust prediction model. We evaluated the performance of the proposed model on real datasets, and our experimental results show that it can achieve excellent prediction performance on the tasks of ESI and DSI. Our proposed method provides a promising alternative for the prediction of E3/DUB-substrate interactions, which has the potential to accelerate drug discovery for various diseases. The source code and dataset are available at https://github.com/PGTSING/Deep-USIpred.
Design of a Wide Angle Beam Scanning Antenna Base on Liquid Crystal

This paper presents a design method of phased array antenna based on liquid crystal. Based on the theory of multi-magnetic dipole antenna and the anisotropy characteristic of liquid crystal, the antenna realizes the function of large Angle beam scanning, which can effectively solve the problem of small beam coverage in the traditional array antenna system. In order to achieve large-angle beam scanning in the horizontal direction and relatively high gain, the 4×4 array mode is adopted. The working bandwidth of the array is 1 GHz, the maximum gain of the array is greater than 17dBi, and the scanning range is ±45°.
Simulation‐based learning modules for undergraduate engineering dynamics

In this paper, we describe software modules that provide both visual and haptic feedback to the student, and evaluate their effectiveness. The system integrates software modules with a haptic interface that can augment teaching and learning in a required undergraduate engineering Dynamics course. Students can change parameters, predict answers, compare outcomes, interact with animations, and feel the results using a haptic interface. Three software modules were evaluated in two separate studies. The first study focused on subjective ratings based on student opinions. The second study assessed the effect of the modules on students' conceptual understanding for force and motion using a pretest/posttest design. The results revealed that the practice with the modules significantly improved the conceptual understanding of the targeted concepts. In addition, students showed a significant preference by stating that the modules would increase their interest in Dynamics as a subject and their engagement in the Dynamics course.
Physics-Model-Regulated Deep Reinforcement Learning Towards Safety & Stability Guarantees

Deep reinforcement learning (DRL) has demonstrated impressive success in solving complex control tasks by synthesizing control policies from data. However, the safety and stability of applying DRL to safety-critical systems remain a primary concern and challenging problem. To address the problem, we propose the Phy-DRL: a novel physics-model-regulated deep reinforcement learning framework. The Phy-DRL is novel in two architectural designs: a physics-model-regulated reward and residual control, which integrates physics-model-based control and data-driven control. The concurrent designs enable the Phy-DRL the mathematically provable safety and stability guarantees. Finally, the effectiveness of the Phy-DRL is validated by an inverted pendulum system. Additionally, the experimental results demonstrate that the Phy-DRL features remarkably accelerated training and enlarged reward.
TF-DTA: A Deep Learning Approach Using Transformer Encoder to Predict Drug-Target Binding Affinity

Bioinformatics is a rapidly growing field that involves the application of computational methods to analyze and interpret biological data. One important task in bioinformatics is predicting the drug-target affinity (DTA), which plays a significant role in drug discovery through virtual screening. Effectively predicting the association between drug molecules and target molecules can speed up the drug discovery process. The DTA can be quantitatively measured. This quantifiable affinity is more precise than a simple binary relationship. In this study, we propose a deep learning model for DTA prediction that utilizes the encoder module of the Transformer architecture. Our proposed model utilizes Convolutional Neural Networks (CNNs) and the encoder module of Transformer to characterize protein and drug sequences. The model outperforms some methods such as KronRLS, SimBoost and DeepDTA as evidenced by superior evaluation metrics such as Mean Squared Error (MSE), Concordance Index (CI), and Regression toward the Mean Index $\left( {r_m^2} \right)$. These results demonstrate the effectiveness of the Transformer’s encoder in extracting meaningful representations from sequences, thereby improving the accuracy of DTA prediction. Deep learning models for DTA prediction can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, the use of machine learning technology enables more effective and efficient drug discovery.
A Diatom-based Paleohydrological Model for the Mackenzie Delta, Northwest Territories, Canada

Floodplain lakes are tightly coupled to their associated river systems and their sediment records should provide integrative records of this interaction. Surface sediments and selected limnological variables were collected from 77 Mackenzie Delta lakes representing three categories of river influence: lakes having continuous connection with the Mackenzie River (n = 23; no-closure), lakes that flood every spring but lose connection during the summer (n = 26; low-closure), and lakes that flood only during an extreme spring flood stage (n = 28; high-closure). Summer lake production, using winter methane concentration as a proxy, and river influence were identified as the principal limnological gradients separating delta lakes. This river influence/primary production gradient also accounted for the greatest amount of variation within the surface sediment diatom assemblages. The diatom flora of the Mackenzie Delta lakes was dominated by benthic taxa, particularly the genera Nitzschia and Navicula, with a greater abundance of stalked, epiphytic taxa in the high-closure lakes. A robust predictive model was developed for inferring lake production from fossil diatom assemblages. The model provides a tool for estimating long-term changes in river influence and lake dynamics from the sediment record of Mackenzie Delta lakes. Diatom-inferred river influence changes within these records may then be linked with past river discharge variability.
FASNet: Fusion Attention Siamese Network For Change Detection of Remote Sensing Images

Remote sensing change detection is an important research direction of remote sensing filed. It is the process of analyzing the differences in remote sensing images from different periods to obtain information on changes in ground targets, which is widely used in the analysis of landform and environmental changes, military reconnaissance and disaster assessment. However, change detection of remote sensing images also faces many challenges, such as light changes, seasonal changes, and noise interference, etc. In order to accurately identify the change areas of bi-temporal remote sensing images, a fusion attention Siamese network (FASNet) is proposed. FASNet adopts Siamese backbone network (ResNet-50) to obtain features from bi-temporal images. Following, pyramid dilated convolution module (PDCM) with different sizes of filters is applied to extract multi-scale context information from high-level sematic features to perceive multi-scale changes of targets. In addition, fusion attention module (FAM) is proposed to highlight the change region and suppress background interference information. Finally, a series of experiments were carried out on CCD dataset, and the experimental results prove the outperformance of our method.
Time Series InSAR for Ground Deformation Observation in Semarang Area, Central Java

The initiatives of open data and open platforms, combined with on-demand data processing in Synthetic Aperture Radar (SAR), enable more straightforward and accessible ground deformation research worldwide. The Alaska Satellite Facility Distributed Active Archive Center (ASF DAAC) provides high-quality interferogram combination pairs based on the Small Baseline Subset (SBAS) technique and on-demand InSAR data analysis known as HyP3. HyP3 offers unwrapped phase data from Sentinel-1 satellites and other derived products that can be utilized with open-source deformation software such as Mintpy. This research aims to assess the quality of interferogram combination pairs by configuring networks of perpendicular and temporal baselines for observing ground deformation in Semarang, Central Java, Indonesia. The study demonstrates that HyP3 products enable time series analysis to determine deformation rates. Over 8.4 years, from November 15th, 2014, to April 14th, 2023, the Simpang Lima landmark in Semarang experienced an approximate subsidence rate of -0.87 cm/year. However, the maximum subsidence rate of approximately 11 cm/year was observed in the southeastern part of Semarang.
Propagation of Stubborn Opinions on Signed Graphs

This paper addresses the problem of propagation of opinions in a Signed Friedkin-Johnsen (SFJ) model, i.e., an opinion dynamics model in which the agents are stubborn and the interaction graph is signed. We provide sufficient conditions for the stability of the SFJ model and for convergence to consensus of a concatenation of such SFJ models.
Metapath-aggregated multilevel graph embedding for miRNA‒disease association prediction

MicroRNAs (miRNAs) are crucial regulators in various diseases. The identification of associations between miRNAs and diseases could greatly facilitate the investigation of disease mechanisms and drug development. Limited by time and cost efficiency, conventional experimental techniques are inadequate for this purpose. With the extensive advance and application of deep learning, developing efficient and accurate computational models for predicting miRNA‒disease associations has a vital role and is feasible. In this study, we proposed a meta-path-aggregated multilevel graph embedding model for miRNA‒ disease association prediction. The model first calculated the multiple similarities among miRNAs and diseases, respectively. Then, the node features were extracted from similarity matrices for miRNAs and diseases. Furthermore, we integrated four types of meta-paths from the miRNA‒lncRNA‒disease heterogeneous graph and learned node embeddings by hierarchical graph attention modules. Finally, the model predicted the miRNA‒ disease associations using two-layer graph convolution networks (GCNs). Compared with six state-of-the-art models, the experimental results demonstrated that our model achieved higher prediction performance with an AUC of 0.9892 and an AUPR of 0.9898 for the 5-fold cross-validation on the HMDDv3.2 dataset. With the case study, the model’s performance was further validated, and the top 20 predicted associations could be experimentally confirmed. All in all, it implies the predictive power of our model1 and the potential value in understanding disease pathology.