Miracle-dz/UniFAR-FTUs huggingface dataset:
Maintenance modeling and cost assessment for repaired reinforced concrete structures exposed to chloride attack in coastal areas in Vietnam

One of the primary causes that can reduce the lifespan of concrete structures is chloride-induced corrosion of reinforcing steel. Recent research has shown that material properties, exposure conditions and climatic conditions have a significant influence on the structural deterioration of reinforced concrete (RC) structures, especially in marine environments. The objective of this paper is to predict the durability of RC structures damaged by chloride-induced corrosion in near-shore environments in Vietnam, considering regular repair activities. A two-dimensional model of chloride penetration in square cross-section RC columns repaired by cover replacement techniques is studied. Two repair methods with different shapes (orthogonal and circular) are considered. The effects of the different repair methods on the life span of structures are investigated in case studies in Vietnam that consider the variations of annual temperature and humidity in three coastal areas such as Danang, Haiphong, and Ho Chi Minh City. Finally, maintenance and repair costs during the lifetime of structures are calculated for each case. The study suggests that for RC structures exposed to two-dimensional chloride ingress, the most critical areas for assessing chloride concentration, predicting corrosion initiation, and planning repair schedules are the corners of the columns. In addition, the chloride-induced corrosion process is strongly influenced by the local weather conditions, especially in areas with high humidity. The repair method with circular concrete replacement seems to be more efficient in terms of maintenance schedule and cost than the repair method with orthogonal concrete replacement.
Object Detection Based on Fusion of Visible and Infrared Images

In consideration of the complementary characteristics between visible light and infrared images, this paper proposes a novel method for object detection based on the fusion of these two types of images, thereby enhancing detection accuracy even under harsh environmental conditions. Specifically, we employ an improved AE network, which encodes and decodes the visible light and infrared images into dual-scale image decomposition. By reconstructing the original images with the decoder, we highlight the details of the fused image. Yolov5 network is then constructed based on this fused image, and its parameters are adjusted accordingly to achieve accurate detection of objects. Due to the complementary information features that are missing between the two image types, our method effectively enhances the precision of object detection.
Suppression Strategy of Differential Mode Circulating Current in Parallel Three-Phase PWM Converter

Parallel three-phase pulse width modulation (PWM) converter is a useful technique for increasing the capacity of a power system, but it also introduces the issue of circulating current, which can cause an increase in the loss of power electronics (PE) switches and even lead to over-current, which can affect the overall performance of the system. In this paper, a strategy is proposed for suppressing the differential mode circulating current (DMCC) in each phase of the parallel converter, which is less dependent on system parameters. Based on the mathematical model of the parallel converter, the relationship between the PWM duty cycle and circulating current is derived, which provides a basis for adjusting the switching operation of PE switches. This method requires no additional hardware and is simple to implement with a fast dynamic response. The simulation results verify the correctness of the theoretical analysis, and it can effectively achieve the balanced suppression of DMCC in each phase, which indicates that the method is suitable for the application scenario of parallel current sharing of multiple converters.
Study on Facial Recognition Method Based on YOLOv5

This study focuses on the facial recognition algorithm. To address limitations of facial recognition, this paper proposes a deep learning method called YOLOv5-Attention for facial feature extraction, recognition. The algorithm in the article is designed based on YOLOv5 and incorporates an attention mechanism module to enhance the expression ability of the model’s eyebrow features and improve its overall performance. The experimental results demonstrate that the algorithm proposed in this paper achieves a harmonic mean of 94% for eyebrow recognition accuracy. This highlights the effectiveness and reliability of the algorithm, which can be applied to eyebrow recognition and dramatic character facial recognition, as well as the research of general facial recognition.
A Camera Calibration Method for Limited Motion of Target in Space

In modern industrial production, vision measurement systems are widely used for product quality inspection. Camera calibration is a crucial step in vision measurement systems that directly affects the accuracy of measurement results. However, in practical applications, the limited space for measurement and restricted movement of calibration targets present new challenges for camera calibration. In this paper, we propose a camera calibration method that is suitable for calibration targets with limited movement space. This method only requires one image of the calibration target to complete system calibration, greatly simplifying the on-site calibration work. Experimental results show that our method has a re-projection error of 0.0187 pixels, indicating high reliability and applicability. The application of this method in the inspection of seamless steel pipes demonstrates its potential for practical industrial production.
Voltage Regulating Strategy of a New LCC/S+L Inductive Power Transfer System

In the voltage control of the inductive power transfer (IPT) system, a cascaded DC-DC converter is normally adopted which reduces the power density. However, the wireless communication between the transmitter and the receiver may lead to a delay of control as well as communication instability. This paper proposes a new LCC/S+L resonance topology for the IPT system, which can realize single-stage voltage regulation on the receiving side with both buck and boost capability. Based on the fundamental wave analysis method, this paper analyzes the working principle of LCC/S+L resonant topology. Then the control strategy of the phase shift angle between bridges and within bridges is obtained by the voltage of both ends of the series inductor and the load voltage vector. It is verified by simulation that the proposed new resonance topology and control strategy can achieve output voltage regulation of 300-500 V with a constant 375 V input DC voltage.
A Strategy for Predicting Residual Service Life of Batteries Based on Impedance Spectrum

With the wide application of lithium batteries in electric vehicles, energy storage and other fields, accurate life prediction of lithium ion batteries becomes particularly important. As a fast and non-destructive electrochemical technology, impedance spectroscopy can reflect the internal characteristics of Li-ion batteries, and it is widely used in battery management. A method based on the health characteristic parameters extracted from the impedance spectrum of Li-ion battery is proposed in this paper to predict the battery life. Firstly, the equivalent circuit model is used to fit the impedance spectrum of the battery and screen out the characteristic quantity which can reflect the aging state of the battery. Then, BP neural network was used to establish the relationship model between the two. Finally, particle filter algorithm was used to predict the remaining service life of the battery. At the end of this paper, the measured results are compared with the predicted results to verify the effectiveness of the proposed method.
Fault Diagnosis Strategy for Solar Cells Based on Reverse Derivation of I-V Curve

This paper proposes a fault diagnosis strategy for solar cells based on the reverse derivation of the I-V curve. This strategy does not require real-time monitoring of the surface irradiance and average temperature of the solar cell during operation. It only needs to calculate(establish) the I-V curve library under different irradiance and solar cell temperature in advance, then measure the open circuit voltage and short circuit current of the photovoltaic module during operation. And the measured voltage and current at the maximum power point can determine whether the solar cell is faulty. The possible faults of various solar cells are simulated by setting up experimental equipment, and the method is used for fault diagnosis. Experimental results show that the proposed method can effectively monitor various faults of solar cells. The method improves the accuracy of fault detection of the solar cell, enhances the reliability and economical benefits of the photovoltaic power station, and realizes online fault detection of the solar cell.
The Optimization of Multi-objective FJSP Based on the Hybrid Algorithm

The flexible job-shop scheduling problem (FJSP) is a critical model in manufacturing systems that assigns operations from different jobs to various machines. However, optimizing multiple targets during the production scheduling process is always necessary. While the non-dominated sorting genetic algorithm (NSGA-II) is an effective method to solve the multi-objective FJSP, it can have the main drawbacks of converging too early and falling into local optimization. To address these issues, this research proposes a hybrid algorithm that combines NSGA-II and multi-objective simulated annealing using a pareto-domination based acceptance criterion (PDMOSA). The PDMOSA has a powerful search performance that can overcome the limitations of NSGA-II. The hybrid algorithm also includes original modification methods such as a deletion criterion, duplicated solution deletion, and new individual adding. Additionally, a plug-in decoding method is introduced. The proposed hybrid algorithm is compared with several improved ways based on NSGA-II in various experiments. The results demonstrate that the performance of the hybrid algorithm is better than the others in multi-objective FJSP.
Research on Thermal Infrared Imaging and Positioning Technology for Emergency Rescue at Sea in Bad Weather

In bad weather, the visibility at sea is low, and it is difficult to rescue people and ships in distress. Existing sensors such as visible light cameras and LIDAR will fail in bad weather, so this paper proposes a design scheme for maritime emergency rescue based on thermal infrared cameras. Infrared thermal imaging is a technology that converts infrared thermal radiation into electrical signals and displays them through the screen, which is unaffected in low visibility environments such as bad weather and night and still works normally, and is a great aid to maritime emergency rescue. The accurate identification of maritime vessels is achieved by selecting the YOLOV5 target detection algorithm with high accuracy, the accurate ranging of maritime vessels by monocular ranging algorithm, and finally the whole project is deployed to NVIDIA Jetson AGX Xavier high-performance edge computing device for inference acceleration using TensorRT framework. To verify the effect of different sizes of network structures on the training of the dataset, experiments were conducted on YOLOV5’s s-model, m-model, l-model and x-model, respectively, and the results showed that the detection results of these four models were comparable, however, the inference frame rates did vary greatly. In windows the inference frame rate is 19fps for the s-model, 15fps for the m-model, 9fps for the l-model and 4fps for the x-model. The s-model was chosen to be used in the subsequent study. The s-model was accelerated by TensorRT to increase the frame rate from 19fps to 30fps, enabling real-time detection and ranging functions. The experimental analysis shows that the scheme of this paper is feasible and reliable.
Research on Energy Management of Pressurized Oxygen-Supplemented Buildings

The energy system of supercharged and oxygenated buildings is a hybrid energy system. The energy used is solar energy, electric energy, pressure energy, etc. In order to make the hybrid energy system more stable and controllable, and to achieve the purpose of energy saving under the condition of stable power consumption of supercharged and oxygenated buildings, a power consumption prediction and management model of supercharged and oxygenated buildings based on neural network is proposed. According to the power consumption system structure of the pressurized oxygenation building, this model establishes a power consumption prediction model based on neural network, and then verifies by simulation that the model can better meet the power consumption prediction conditions of the pressurized oxygenation building. Finally, according to the experiment, this power consumption prediction model can make the power consumption management of the pressurized oxygenation building more stable and controllable. When meeting the daily and stable power consumption requirements of the pressurized oxygenation building, the model can be used to predict the power consumption of the pressurized oxygenation building. Green energy is also reasonably used to save energy in pressurized and oxygen-supplemented buildings.
Effectiveness of Botulinum Toxin on Hand Tremor Intensity and Upper Limb Function in Patients with Parkinson’s Disease: Results of a Systematic Review

Background: Hand tremor is a common symptom of Parkinson’s disease (PD). Tremors may be resistant to drug treatments. Therefore, Botulinum toxin (BoNT) could be a good alternative. This study aimed to review and analyze studies on the efficacy and safety of BoNT injection in hand tremor intensity and upper limb function in patients with idiopathic PD. Methods: A comprehensive search was conducted for studies on the effect of local BoNT injections on tremors in PD patients from 1990 to December 2021. Electronic databases such as Cochrane Central Control Records, PubMed, Scopus, Web of Science, EMBASE, Google Scholar, Clinicaltrial.gov, ProQuest, Science Direct, CINAHL, and Psychoinfo were searched systematically. Results: Ten studies, comprising one double-blinded randomized clinical trial and nine pilot open-labeled studies with 131 participants, met eligibility criteria. The reported tremor intensity ranged from 1 to 3, and the average tremor duration of 5.93 ± 2.08 years. The injectable dose was 68–100 units of onabotulinum-toxin-A in each upper limb muscle, mostly wrist flexors. The results showed a decrease in unified Parkinson’s disease rating scale (UPDRS)_20 and UPDRS_21 indices by 1.22 ± 1.1 and 1.20 ± 0.9, respectively, without causing severe side effects. The BoNT relative effectiveness in the forearm and arm muscles was reported 6–16 weeks after injection. Discussion: The kinematic, electromyography-guided, and electrical stimulation evaluations allow for accurate muscle localization and minimize the possibility of BoNT diffusion and antibody formation. More extensive randomized clinical trials with uniform measurement criteria are recommended to reduce bias and provide more accurate conclusions. Highlight Tremor treatment in Parkinson’s-disease (PD) is challenging. Drugs effect is temporary, and surgery is critical management. This study reviews the Botulinum-toxin injection efficacy in hand tremor intensity and upper limb function. The results showed a decrease in unified Parkinson’s disease rating scale (UPDRS)_20 and UPDRS_21 by 1.22 ± 1.1 and 1.20 ± 0.9, respectively, 6–16 weeks after injection.
Acoustic and perceptual impact of face masks on speech: A scoping review

During the COVID-19 pandemic, personal protective equipment such as facial masks and coverings were mandated all over the globe to protect against the virus. Although the primary aim of wearing face masks is to protect against viral transmission, they pose a potential burden on communication. The purpose of this scoping review was to identify the state of the evidence of the effect of facial coverings on acoustic and perceptual speech outcomes. The scoping review followed the framework created by Arksey & O’Malley (2005) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines (PRISMA-ScR; Tricco et al., 2018). The search was completed in May 2021 across the following databases: PubMed, EMBASE, PsycINFO, Web of Science, and Google Scholar. A total of 3,846 records were retrieved from the database search. Following the removal of duplicates, 3,479 remained for the title/abstract screen and 149 were selected for the full-text review. Of these, 52 were included in the final review and relevant data were extracted. The 52 articles included in the final review consisted of; 11 studied perceptual outcomes only, 16 studied acoustic outcomes only, and 14 studied both perceptual and acoustic outcomes. 13 of these investigated acoustic features that could be used for mask classification. Although the findings varied from article to article, many trends stood out. Many articles revealed that face masks act as a low pass filter, dampening sounds at higher frequencies; however, the frequency range and the degree of attenuation varied based on face mask type. All but five articles that reported on perceptual outcomes showed a common trend that wearing a face mask was associated with poorer speech intelligibility. The findings of the scoping review provided evidence that facial coverings negatively impacted speech intelligibility, which is likely due to a combination of auditory and visual cue degradation. Due to the continued prevalence of mask use, how facial coverings affect a wider variety of speaker populations, such as those with communication impairments, and strategies for overcoming communication challenges should be explored.
Gossypol improves myocardial dysfunction caused by sepsis by regulating histone acetylation

Gossypol is a polyphenol from the cotton plant with anti‐inflammatory and anti‐oxidation activities and can also function as a histone deacetylase (HDAC) inhibitor. Sepsis is an inflammatory disease with high mortality. Inflammation, oxidative stress, and epigenetic factors are involved in sepsis and its complications. The biological activities of gossypol strongly suggest the potential effects of gossypol on sepsis. In the present study, the beneficial effects of gossypol on sepsis were evaluated. We established a cecal ligation and puncture (CLP) mouse model of sepsis and treated CLP mice with gossypol. The survival rate, serum level of myocardial injury markers, and myocardial level of oxidation markers were measured. We also administered gossypol to lipopolysaccharide (LPS)‐treated primary cardiomyocytes. The production of pro‐inflammatory cytokines, activation of protein kinase B (AKT) and IκB kinase (IKK), acetylation of histone, and expression of HDACs were measured. Gossypol prevented the death of CLP mice and ameliorated myocardial damage in CLP mice. Moreover, gossypol decreased oxidative factors, while promoting antioxidant production in CLP mice. Gossypol prevented LPS and cytosine‐phosphate‐guanosine‐induced expression of pro‐inflammatory cytokines, suppressed LPS‐induced activation of AKT and IKK, inhibited histone acetylation, and decreased the expression of HDACs. In conclusion, gossypol ameliorates myocardial dysfunction in mice with sepsis.
Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors

Cross-domain pedestrian detection aims to generalize pedestrian detectors from one label-rich domain to another label-scarce domain, which is crucial for various real-world applications. Most recent works focus on domain alignment to train domain-adaptive detectors either at the instance level or image level. From a practical point of view, one-stage detectors are faster. Therefore, we concentrate on designing a cross-domain algorithm for rapid one-stage detectors that lacks instance-level proposals and can only perform image-level feature alignment. However, pure image-level feature alignment causes the foreground-background misalignment issue to arise, i.e., the foreground features in the source domain image are falsely aligned with background features in the target domain image. To address this issue, we systematically analyze the importance of foreground and background in image-level cross-domain alignment, and learn that background plays a more critical role in image-level cross-domain alignment. Therefore, we focus on cross-domain background feature alignment while minimizing the influence of foreground features on the cross-domain alignment stage. This paper proposes a novel framework, namely, background-focused distribution alignment (BFDA), to train domain adaptive one-stage pedestrian detectors. Specifically, BFDA first decouples the background features from the whole image feature maps and then aligns them via a novel long-short-range discriminator. Extensive experiments demonstrate that compared to mainstream domain adaptation technologies, BFDA significantly enhances cross-domain pedestrian detection performance for either one-stage or two-stage detectors. Moreover, by employing the efficient one-stage detector (YOLOv5), BFDA can reach 217.4 FPS ( $640\times 480$ pixels) on NVIDIA Tesla V100 (7~12 times the FPS of the existing frameworks), which is highly significant for practical applications. The code from this study will be made publicly available.
Resource allocation in rooted trees for VLSI applications

ABSTRACT Several optimization problems of modifying the weight of vertices in rooted trees, some of which are special cases of the inverse 1-median problem, are solved. Such problems arise in Very Large Scale Integration (VLSI) design of hardware security circuits, circuit synchronization, and analog-to-digital converters. These problems require assigning costly hardware (demands) to the leaves of rooted trees. One common property of these problems is that a resource allocated to an internal node can be shared by the entire sub-tree emanated at the node. Two types of problems are studied here. (1) A tree node employs an addition operation and the demands at the leaves are obtained by summing the resources allocated to nodes along the root-to-leaf paths. A linear-time bottom-up algorithm is shown to minimize the total resources allocated to tree nodes. (2) A tree’s node employs a multiplication operation and the demands at the leaves are obtained by multiplying the resources allocated to nodes along the root-to-leaf paths. A bottom-up dynamic programming algorithm is shown to minimize the total resources allocated to the tree’s nodes. While the above problems are usually solved by design engineers heuristically, this paper offers optimal solutions that can be easily programmed in CAD tools.