Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Trajectory Tracking for Unknown H∞ Constrained Systems Based on Reinforcement Learning

This letter obtains a solution for the optimal trajectory tracking of unknown H∞)-constrained systems using reinforcement learning to derive the Nash equilibrium. To illustrate the H∞) tracking, a discounted performance function with multi-inputs in $L$2-gain is provided. An augmented vector, consisting of known bounded reference trajectory and tracking error, is constructed to describe the augmented system. Along the augmented system, a new index function is introduced to formulate the optimal tracking control problem. This letter presents a policy iteration algorithm that provides the Nash equilibrium solution, even without the system dynamics information. By applying Pontryagin's maximum principle and introducing a novel costate, the system's stability is ensured through the determination of its upper bound. Thorough examination verifies that the suggested tracking algorithm converges effectively when using the Hamilton-Jacobi-Isaacs (HJI) equation.
An Investigation of Context-Aware Object Detection based on Scene Recognition

Visual scene understanding for humans entails identifying regions of interest and then reasoning about information gained through their contextual analysis. Attempts to reproduce this process using Computer Vision techniques play an important role in improving the performance of Object Detection and general scene understanding algorithms. In this paper, we augment the training algorithm of an Object Detection model by integrating contextual information in the form of ‘scene labels’, through various methods to identify the superior approach. Several deep learning models were implemented and evaluated; results show that the Contextually Aware Object Detection Model performed the best, producing the highest classification accuracy of 75.14% and mean bounding box IoU of 0.69 on the constructed dataset. This was achieved by virtue of an auxiliary scene classification model used to make image scene predictions during the training phase.
A Review on Social Media and Deep Neural Networks for Sentiment Analysis in Emergency Management

Disaster management is essential to ensuring public safety and reducing the impact of disasters. In recent years, disaster response and recovery have benefited from the use of social media and deep neural network technologies. Examples of sentiment analysis include tweets and deep neural networks. Information about disasters can be found on Twitter in real time. Information, media, and ideas are shared through these channels. Monitoring and evaluating Twitter messages can help emergency management professionals assess the situation, identify affected places, and establish the requirements of the people. Text sentiment analysis is useful in emergency management. Deep neural networks, a type of machine learning, can analyse and categorise people's feelings on social media. By being trained on massive volumes of historical data, deep neural networks can classify tweets as positive, negative, or neutral and produce a sentiment score. Monitoring social media and evaluating sentiment is only one of the many ways in which deep neural networks aid disaster management authorities. The precision of sentiment analysis can also be impacted by sarcasm, ambiguity, and subtleties in language use. In conclusion, disaster management can benefit from the use of Twitter and deep neural networks for sentiment analysis. By monitoring and assessing social media content, authorities can improve situational awareness, make educated judgements, and safeguard affected communities during disasters.
A Comparative Study of Malware Detection Using Hybrid Deep Learning Techniques

Our digital world faces a major danger from malware (viruses, bots, ransomware, trojans), which is continually improving and becoming more complex. Malware detection has been long automated using machine learning methods and deep learning models have lately showed potential in this field. In this study two deep learning techniques are used, namely Convolution Neural Network (CNN) and Long-Short-Term Memory Network (LSTM). The focus is on utilizing a dataset designed for malware detection, named FAF674. In the realm of malware detection, optimal outcomes were achieved through the utilization of LTSM, CNN, and associated methodologies. Leveraging these two prominent deep learning algorithms yielded an impressive average Accuracy of 96%, an average Precision of 97%, and an average Recall of 97%. Notably, the pinnacle of effectiveness emerged with remarkable metrics, showcasing an Accuracy of 0.982, Precision of 0.988, and Recall of 0.990, thereby underscoring the significant advancements achieved in this study.
Statistical Analysis of Bitwise Early Termination for Iterative Multiuser Detection in IDMA Systems

This paper statistically analyzes the bitwise early termination (BET) technique, which was recently proposed to reduce the latency of the iterative multiuser detection (MUD) in interleave division multiple access (IDMA) systems. The average numbers of iterations are measured for all the bits in a frame, and the corresponding results are analyzed in detail. From the statistics acquired, future researches can be initiated to further improve the performance of the BET and IDMA systems.
Design of Generalized Symmetric Doherty Power Amplifier with Device Parasitics Compensation for 5G Wireless Transmitters

The symmetric Doherty Power Amplifier (DPA) with extended output power back-off (OPBO) for 5G applications is investigated in this article. The problems experienced in the actual design phase when the design involves a high frequency and power, including device non-idealities, are examined. A parasitic compensation technique addresses the DPA issues, such as RF current leakage from the main to the auxiliary (when the aux $P A$ is not open ideally). In addition, considering the device parasitic along with the load combiner design directly matches the optimum loads $\left(R_{\text {opt} }\right.$) at saturation and $2 R_{\text {opt} }$ at back-off, omitting the design requirement of the output matching network. For the verification, A $47-\mathrm{dBm}$ peak power, DPA with $8 \mathrm{~dB}$ OPBO, operating from $3.3 \mathrm{GHz}-3.7 \mathrm{GHz}$ is designed using CG2H40025F GaN devices. Good understanding is observed between the simulated and measured results. The DPA design shows $65-75 \%$ Drain Efficiency (DE) at saturation ($\gt47$ dBm) and $45-47 \% \mathrm{DE}$ at 8 -dB back-off output power. From the author’s knowledge, this device will be a potential candidate for small base station applications.
GO-LIO: Lidar-IMU SLAM Algorithm Based on Ground Optimization

To address the drift problem of existing Simultaneous Localization And Mapping (SLAM) algorithms in the vertical Z-axis direction during autonomous navigation of unmanned vehicles in urban road scenarios, this paper proposes GO-LIO: a robust algorithm based on ground optimization with tightly coupled Lidar and IMU. The algorithm is divided into three main parts: distortion removal, ground optimization, and loop closure detection. To address the problem of insufficient constraints in the Z-axis direction, correction is performed using residuals constructed from ground distances. To address the problem of error accumulation caused by low-quality ground estimation in ground constraints, a fitting detection method is employed to verify the authenticity of the fitted ground. To address the issues of continuity and consistency in large-scale mapping, a loop closure detection method is employed to perform secondary correction of errors in the Z-axis direction. The experimental results indicate that this method exhibits excellent robustness in urban road scenes. Compared to ALOAM and FAST-LIO2 algorithms, it achieves a 17.5% and 33.1% improvement in Z-axis accuracy on the KITTI seq 00, respectively. Under the highly challenging dataset collected by ourselves, it was improved by 6.7% compared with FAST-LIO2.
Design, Fabrication, and Characterization of CMOS MEMS-Based Thermoelectric Power Generators

This paper presents the design, modeling, fabrication, and characterization of CMOS microelectromechanical-systems-based thermoelectric power generators (TPGs) to convert waste heat into a few microwatts of electrical power. Phosphorus and boron heavily doped polysilicon thin films are patterned and electrically connected to consist thermopiles in the TPGs. To optimize heat flux, the thermal legs are embedded between the top and bottom vacuum cavities, which are sealed on the wafer level at low temperature. A heat-sink layer is coated on the cold side of the device to effectively disperse heat from the cold side of the device to ambient air. The peripheral cavity is designed to isolate heat from the surrounding silicon substrate. Both simulation and experiments are implemented to validate that the energy conversion efficiency is highly improved due to the aforementioned three unique designs. The device has been fabricated by a CMOS-compatible process. Properties of thermoelectric material, such as the Seebeck coefficient, electrical resistivity, and specific contact resistance are measured through test structures. For a device in the size of 1 cm2 and with a 5-K temperature difference across the two sides, the open-circuit voltage is 16.7 V and the output power is 1.3 ¿W under matched load resistance. Such energy can be efficiently accumulated as useful electricity over time and can prolong the battery life.
The history of South American tropical precipitation for the past 25,000 years.

Long sediment cores recovered from the deep portions of Lake Titicaca are used to reconstruct the precipitation history of tropical South America for the past 25,000 years. Lake Titicaca was a deep, fresh, and continuously overflowing lake during the last glacial stage, from before 25,000 to 15,000 calibrated years before the present (cal yr B.P.), signifying that during the last glacial maximum (LGM), the Altiplano of Bolivia and Peru and much of the Amazon basin were wetter than today. The LGM in this part of the Andes is dated at 21,000 cal yr B.P., approximately coincident with the global LGM. Maximum aridity and lowest lake level occurred in the early and middle Holocene (8000 to 5500 cal yr B.P.) during a time of low summer insolation. Today, rising levels of Lake Titicaca and wet conditions in Amazonia are correlated with anomalously cold sea-surface temperatures in the northern equatorial Atlantic. Likewise, during the deglacial and Holocene periods, there were several millennial-scale wet phases on the Altiplano and in Amazonia that coincided with anomalously cold periods in the equatorial and high-latitude North Atlantic, such as the Younger Dryas.
Implementation of Advanced IP Network Technology for IPTV Service

: It is absolutely essential to implement advanced IP network technologies such as QoS, Multicast, High Availability, and Security in order to provide real-time services like IPTV via IP backbone network. In reality, the existing commercial networks of internet service providers are subject to certain technical difficulties and limitations in embodying those technologies. On-going research efforts involve the experimental engineering works and implementation experience to trigger IPTV service on the premium-level IP backbone which has recently been developed. This paper introduces the core network technologies that will enable the deployment of a high-quality IPTV service, and then proposes a suitable methodology for application and deployment policies on each technology to lead the establishment and globalization of the IPTV service.
Development of a Cell-Based High-Throughput Assay to Screen for Inhibitors of Organic Anion Transporting Polypeptides 1B1 and 1B3

The two organic anion transporting polypeptides (OATPs) 1B1 and 1B3 are expressed at the sinusoidal membrane of hepatocytes. They have a broad and overlapping substrate specificity and transport many endobiotics and drugs. Specific inhibitors are required to determine the contribution of each OATP to the hepatocellular uptake of common substrates. We have developed a cell-based high-throughput assay to screen chemical libraries in order to identify such inhibitors for OATP1B1 and OATP1B3. We have used OATP1B1- or OATP1B3-expressing Chinese Hamster Ovary cells on 96-well plates and determined uptake of fluorescein-methotrexate (FMTX). We validated the assay with known inhibitors and screened the well characterized Prestwick library of 1120 drugs. Along with several known OATP inhibitors including rifampicin, cyclosporine A and mifepristone we identified some new inhibitors. For inhibitors that seemed to be able to distinguish between OATP1B1- and OATP1B3-mediated FMTX uptake IC50 values were determined. Estropipate (estrone-3-sulfate stabilized with piperazine) was the most selective OATP1B1 inhibitor (IC50 = 0.06 μM vs. 19.3 μM for OATP1B3). Ursolic acid was the most selective OATP1B3 inhibitor (IC50 = 2.3 μM vs. 12.5 μM for OATP1B1). In conclusion, this cell-based assay should allow us to identify even more specific inhibitors by screening larger chemical libraries.
Maintenance of zonal wind variability associated with the annular mode on Mars

Results from a Mars general circulation model (MGCM) were used to investigate the maintenance of the annular mode in the Martian atmosphere. Analysis of the Martian northern winter, as represented by the MGCM, revealed a prominent transient wave with zonal wave number 1 that had a baroclinic instability structure. An empirical orthogonal function (EOF) decomposition showed that the first two modes of the EOF contained features of the transient wave with wave number 1. The third EOF mode had an annular structure about the Martian North Pole and an equivalent barotropic structure in the vertical. Each term in the transformed Eulerian mean equation was estimated from the MGCM output to investigate the maintenance of the annular mode. Wave forcing (Eliassen‐Palm flux divergence) played an important role in maintaining the mode. In the wave forcing term, contributions from transient baroclinic waves with wave numbers 1 and 2 were important.
Lane Environment Detection with Only On-Board Camera

Lane environment detection is a vital prerequisite for realizing autonomous driving in complex scenarios. However, existing methods face significant challenges, primarily containing the absence of lane-level positioning information, the imbalance between accuracy and processing speed, and the prohibitively high cost of equipment. To address these challenges, this paper introduces a hybrid method that combines deep learning and traditional image processing techniques. The proposed approach uses the YOLO (You Only Look Once) for environmental vehicle detection, augmented by a post-processing module that provides detailed lane-level positioning information. Notably, the post-processing stage integrates deep learning and traditional computer vision methods, ensuring a fast detection speed while maintaining a desirable detection accuracy. An additional advantage of the proposed method is its reliance solely on an on-board camera, effectively lowering the equipment cost. Furthermore, the method provides more comprehensive detection by incorporating geometric method to estimate the distance between the ego-vehicle and surrounding vehicles. The proposed approach addresses the aforementioned challenges and provides a comprehensive solution for effective and low-cost lane environment detection. To be specific, our proposed method runs at 58 fps and achieves accuracy at 95.22%.
Action Unit Based Smiling Face Generation Method

The widespread usage of facial expression processing in digital entertainment and data augmentation has drawn rising attention. However, the existing works on facial expression generation still fall short in reality and continuity, especially on smiling faces, which is not natural enough. In this paper, we propose a smiling face generation method based on Action Unit (AU) to address the aforementioned problems. Our method mainly adjusts the magnitude of target AU according to the features of smiling faces and the person itself to achieve better generation effect. We propose two ways to adjust AU, called average AU and relative AU respectively. Average AU reflects the AU configuration with universal smiling face expression and relative AU combines the AUs represent smiling expression with other AU of source image. We collect smiling face images to accumulate new datasets where we find the intrinsic pattern of AU. We leverage average AU and relative AU to optimize the process of smiling faces generation and the experiment results indicate that our method can achieve better facial expression manipulation compared with the baseline models. In addition, we conduct user evaluation experiment from which we get following results: 1) the results of L2 distance and Fréchet Inception Distance (FID) indicate that degree of the facial expression realization of the images generated by our method is improved greatly; 2) the results of the questionnaire indicate that the images generated by our method win more human visual preferences and are of higher quality.
Impact of Stress on Sleep Levels: A Comparative Machine Learning Study Based on Wearable Data

Inadequate sleep is a significant factor in chronic conditions such as cardiovascular disease, diabetes, and mental health disorders, and includes not just sleep duration but also the quality of sleep. Although addressing sleep disorders is complex, mitigating their physical and psychological consequences through effective monitoring and control measures is achievable using wearable devices. While there have been a variety of successful works using wearable devices to classify sleep levels, this study investigates the feasibility of machine learning (ML) approaches based on wearable data to predict different levels of sleep by considering physiological parameters including stress. Stress has long been established as one of the most crucial physiological parameters influencing sleep levels. In this paper, we leveraged data collected from wrist-worn devices to classify the levels of sleep using two different datasets: one considering the level of stress and the other excluding it. The objective is to achieve optimal classification performance by employing a diverse set of seven ML-based classifiers, including Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Naive Bayes, and XGBoost. By comparing the performances of the models on these two datasets, we observed that the model incorporating stress inputs exhibited superior accuracy in predicting sleep, highlighting the significant role of stress in sleep levels. Moreover, the findings from the models and their comparison indicate that the Random Forest and XGBoost algorithms outperform other ML methods, exhibiting an impressive accuracy of 95 % in accurately predicting different sleep levels.
The small molecule, genistein, increases hepcidin expression in human hepatocytes

Hepcidin, a peptide hormone that decreases intestinal iron absorption and macrophage iron release, is a potential drug target for patients with iron overload syndromes because its levels are inappropriately low in these individuals. Endogenous stimulants of Hepcidin transcription include bone morphogenic protein 6 (BMP6) and interleukin‐6 (IL‐6) by effects on mothers against decapentaplegic homolog (Smad)4 or signal transducer and activator of transcription (Stat)3, respectively. We conducted a small‐scale chemical screen in zebrafish embryos to identify small molecules that modulate hepcidin expression. We found that treatment with the isoflavone, genistein, from 28‐52 hours postfertilization in zebrafish embryos enhanced Hepcidin transcript levels, as assessed by whole‐mount in situ hybridization and quantitative real‐time reverse‐transcriptase polymerase chain reaction. Genistein's stimulatory effect was conserved in human hepatocytes: Genistein treatment of HepG2 cells increased both Hepcidin transcript levels and promoter activity. We found that genistein's effect on Hepcidin expression did not depend on estrogen receptor signaling or increased cellular iron uptake, but was impaired by mutation of either BMP response elements or the Stat3‐binding site in the Hepcidin promoter. RNA sequencing of transcripts from genistein‐treated hepatocytes indicated that genistein up‐regulated 68% of the transcripts that were up‐regulated by BMP6; however, genistein raised levels of several transcripts involved in Stat3 signaling that were not up‐regulated by BMP6. Chromatin immunoprecipitation and ELISA experiments revealed that genistein enhanced Stat3 binding to the Hepcidin promoter and increased phosphorylation of Stat3 in HepG2 cells. Conclusion: Genistein is the first small‐molecule experimental drug that stimulates Hepcidin expression in vivo and in vitro. These experiments demonstrate the feasibility of identifying and characterizing small molecules that increase Hepcidin expression. Genistein and other candidate molecules may subsequently be developed into new therapies for iron overload syndromes. (Hepatology 2013;58:1315–1325)
Ultrahigh Voltage Gain DC-DC Converter Based on Reformed Quadratic Boost Converter with Switched Capacitor and Coupled Inductor

This paper presents a new ultrahigh voltage gain DC-DC converter composed of a reformed quadratic boost converter, a switched capacitor unit, and a coupled inductor. A secondary winding of the coupled inductor is unified with the switched capacitor and the leakage energy of the coupled inductor recycles through the output appropriately. The converter performance includes high voltage gain besides low voltage stress on its switch and diodes. In addition, it requires a small inductor on the input side that results in lower power loss. Its operations states and main equations are studied to investigate the converter properties. Using simulation in MATLAB Simulink the analysis is validated, and the results are used to demonstrate the suitable performance of the converter.
Influence of Various Non-Oriented Electrical Steels on Motor Efficiency and Iron Loss in Switched Reluctance Motor

The performance of a conventional 6/4 switched reluctance motor (SRM) has been studied using various non-oriented electrical steels for core material. The motor efficiency exhibited a strong correlation with the high-frequency material iron loss W10/400 (iron loss at 1.0 T, 400 Hz) through the influence on the motor iron loss although the fundamental frequency of the stator is about 100-200 Hz, while the material flux density B50 (magnetic flux density at 5000 A/m) had little influence on the motor efficiency. This paper also presents the calculation results of the motor iron loss based on FE analysis, which applies different equation forms to calculate the hysteresis loss according to typical flux density waveforms, and the computed iron losses agreed well with the measured losses. Then, it is shown that W10/400 is related well to the variation of hysteresis loss fraction in the motor iron loss for different materials and that the effect of core material on iron loss at higher frequency can be evaluated by their W10/400 values.
Spin Estimation for Back Spin Serves in Table Tennis Using Racket Speed and Angle

Table tennis is one of the sports where ball spin is important. There is a big difference in the amount of ball spin between beginners and advanced players. Although various methods have been used to calculate ball spin in table tennis, most of them are used for table tennis robots to hit the ball back. This study formulates the relationship between racket speed, the angle of the racket's face, the angle of the racket's trajectory and ball spin for the purpose of improving performance for beginners and other players. Experiments showed that ball spin can be calculated with high accuracy for back spin serves.